upload 14 file

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+SAC_Logo.png filter=lfs diff=lfs merge=lfs -text

Bhankhara_Df_11_he_5_2020-21.geojson

@@ -0,0 +1,8 @@
+{
+"type": "FeatureCollection",
+"name": "Bhankhara Df 11 he 5 2020-21",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "Name": "bhankhara Df " }, "geometry": { "type": "Polygon", "coordinates": [ [ [ 73.232880205954501, 23.9293492015216 ], [ 73.2320424558106, 23.9295414782706 ], [ 73.231770305881497, 23.929079612195199 ], [ 73.230492834793793, 23.929757112420202 ], [ 73.2296710069635, 23.930461233260601 ], [ 73.2287325848189, 23.929426068463702 ], [ 73.230010982152606, 23.928454021654801 ], [ 73.231320825956999, 23.9274161693899 ], [ 73.231748632627799, 23.9277412596551 ], [ 73.232405167342506, 23.927898156106 ], [ 73.232880205954501, 23.9293492015216 ] ] ] } }
+]
+}

CoalTPP_Locations - small_dataset.csv

@@ -0,0 +1,6 @@
+name,latitude,longitude
+PANIPAT TPS,29.3975059,76.8763161
+RAJIV GANDHI TPS,29.34698,75.857538
+YAMUNA NAGAR TPS,30.1075175,77.3267646
+GH TPS (LEH.MOH.),30.2674,75.1644
+GOINDWAL SAHIB TPP,31.38414,75.146503

CoalTPP_Locations.csv

@@ -0,0 +1,185 @@
+name,latitude,longitude
+PANIPAT TPS,29.3975059,76.8763161
+RAJIV GANDHI TPS,29.34698,75.857538
+YAMUNA NAGAR TPS,30.1075175,77.3267646
+GH TPS (LEH.MOH.),30.2674,75.1644
+GOINDWAL SAHIB TPP,31.38414,75.146503
+ROPAR TPS,31.0417,76.5847
+CHHABRA-II TPP,24.6236,77.0383
+CHHABRA-I PH-1 TPP,24.62092,77.035439
+CHHABRA-I PH-2 TPP,24.63433,77.0385039
+KALISINDH TPS,24.5325129,76.100353
+KOTA TPS,25.1717,75.8169
+SURATGARH STPS,29.181766,74.018741
+SURATGARH TPS,29.181766,74.018741
+ANPARA TPS,24.202043,82.789128
+HARDUAGANJ TPS,28.0175,78.13
+JAWAHARPUR STPP,27.497098,78.826071
+OBRA TPS,24.4445803,82.9803325
+PARICHHA TPS,25.513571,78.757918
+DSPM TPS,22.368196,82.721986
+KORBA-WEST TPS,22.412536,82.682187
+MARWA TPS,22.070801,82.602189
+GANDHI NAGAR TPS,23.249722,72.673889
+UKAI TPS,21.2093,73.5574
+WANAKBORI TPS,22.8725375,73.3588028
+AMARKANTAK EXT TPS,23.162146,81.632816
+SANJAY GANDHI TPS,23.305744,81.064817
+SATPURA TPS,22.108133,78.177072
+SHREE SINGAJI TPP,22.09914,76.531727
+BHUSAWAL TPS,21.04798,75.85502
+CHANDRAPUR(MAHARASHTRA) STPS,20.0063,79.29
+KHAPARKHEDA TPS,21.276138,79.121518
+KORADI TPS,21.2420685,79.0949659
+NASIK TPS,19.9814589,73.8920556
+PARAS TPS,20.7147,76.7948
+PARLI TPS,18.9075857,76.5429497
+Dr. N.TATA RAO TPS,16.5984901,80.5365853
+RAYALASEEMA TPS,14.697282,78.459364
+DAMODARAM SANJEEVAIAH TPS,14.327604,80.122105
+BELLARY TPS,15.1931738,76.7195034
+RAICHUR TPS,16.3532,77.3422
+YERMARUS TPP,16.295261,77.35612
+METTUR TPS,11.7720201,77.8141046
+METTUR TPS - II,11.7744564,77.8172243
+NORTH CHENNAI TPS,13.253013,80.327804
+TUTICORIN TPS,8.762518,78.175976
+SINGARENI TPP,18.837231,79.574766
+BHADRADRI TPP,18.027488,80.735645
+KAKATIYA TPS,18.3831934,79.8271644
+KOTHAGUDEM TPS (NEW),17.6219,80.69355
+KOTHAGUDEM TPS (STAGE-7),,
+RAMAGUNDEM-B TPS,,
+TENUGHAT TPS,23.7276632,85.7652325
+IB VALLEY TPS,21.690729,83.860096
+D.P.L. TPS,23.521147,87.302309
+BAKRESWAR TPS,23.8292205,87.451034
+BANDEL TPS,22.995746,88.403815
+KOLAGHAT TPS,22.416565,87.870984
+SAGARDIGHI TPS,24.3688141,88.1040001
+SANTALDIH TPS,23.6013,86.4666
+DADRI (NCTPP),28.603,77.6078
+RIHAND STPS,24.027,82.7915
+SINGRAULI STPS,24.1042119,82.7063552
+TANDA TPS,26.5894846,82.601235
+UNCHAHAR TPS,25.9137597,81.3272384
+GADARWARA TPP,22.861752,78.866129
+KHARGONE STPP,22.065169,75.851943
+KORBA STPS,22.3874221,82.6818961
+LARA TPP,21.757661,83.457545
+MAUDA TPS,21.18074,79.396541
+SIPAT STPS,22.131662,82.29035
+SOLAPUR STPS,17.551296,75.9832
+VINDHYACHAL STPS,24.0983,82.6719
+KUDGI STPP,16.499546,75.834632
+RAMAGUNDEM STPS,18.7584502,79.4573172
+SIMHADRI,17.591,83.09166
+TELANGANA STPP PH-1,18.7584502,79.4573172
+BARAUNI TPS,25.391376,86.023488
+BARH STPS,25.4693,85.74516
+DARLIPALI STPS,21.963554,83.89187
+FARAKKA STPS,24.772729,87.893707
+KAHALGAON TPS,25.236903,87.265595
+NORTH KARANPURA TPP,23.850772,85.012411
+TALCHER STPS,21.098074,85.074005
+BONGAIGAON TPP,26.44437,90.367205
+INDIRA GANDHI STPP,28.485091,76.37316
+MEJA STPP,25.145867,81.940096
+BHILAI TPS,21.184,81.4237
+SEIONI TPP,22.735479,79.912341
+VALLUR TPP,13.237131,80.301754
+MUZAFFARPUR TPS,26.194771,85.303302
+NABINAGAR STPP,24.776,84.158025
+NABINAGAR TPP,24.705081,84.088738
+BOKARO TPS `A` EXP,23.782944,85.883391
+CHANDRAPURA(DVC) TPS,20.0063,79.29
+DURGAPUR STEEL TPS,23.531506,87.249856
+KODARMA TPP,24.391534,85.561899
+MEJIA TPS,23.4639,87.1311
+RAGHUNATHPUR TPP,23.62191,86.660703
+NTPL TUTICORIN TPP,8.760363,78.171083
+ADANI POWER LIMITED KAWAI TPP,24.777997,76.737144
+ADANI POWER LIMITED RAIGARH TPP,21.743775,83.274093
+ADANI POWER LIMITED RAIPUR TPP,21.449918,81.852474
+ADANI POWER LIMITED TIRODA TPP,21.412502,79.96731
+AKALTARA TPS,21.960296,82.409064
+AMRAVATI TPS,21.080316,77.901242
+ANPARA C TPS,24.202043,82.789128
+ANUPPUR TPP,23.073731,81.784286
+BALCO TPS,22.3930944,82.7439443
+BANDAKHAR TPP,22.340954,82.430603
+BARADARHA TPS,21.911436,83.188863
+BARKHERA TPS,28.55,80.1
+BELA TPS,20.806389,79.051944
+BINA TPS,24.317222,78.133564
+BINJKOTE TPP,22.010422,83.205779
+BUDGE BUDGE TPS,22.470344,88.139949
+DAHANU TPS,19.9545,72.7478
+DERANG TPP,21.127044,84.980552
+DHARIWAL TPP,20.010588,79.203433
+DISHERGARH TPP,,
+GMR WARORA TPS,20.279656,78.975351
+HALDIA TPP,22.060459,88.109748
+HIRANMAYE TPP,22.076989,88.098413
+JOJOBERA TPS,22.7554,86.2491
+KAMALANGA TPS,20.870576,85.266979
+KHAMBARKHERA TPS,27.9,80.8
+KUNDARKI TPS,27.03,82.23
+LALITPUR TPS,24.795833,78.646944
+MAHADEV PRASAD STPP,22.836504,86.061294
+MAHAN TPP,24.008674,82.41703
+MAHATMA GANDHI TPS,28.488143,76.352534
+MAITHON RB TPP,23.8209,86.76
+MAQSOODPUR TPS,27.81,79.94
+NAWAPARA TPP,21.906738,83.128556
+NIGRI TPP,24.14997,81.904525
+OP JINDAL TPS,22.104,83.445
+PAINAMPURAM TPP,14.351195,80.143086
+PATHADI TPP,22.2454,82.7246
+PRAYAGRAJ TPP,25.196345,81.658534
+RAJPURA TPP,30.558963,76.577593
+ROSA TPP Ph-I,27.8194455,79.9371618
+SABARMATI (D-F STATIONS),23.070555,72.593889
+SASAN UMTPP,23.9775233,82.6264733
+SGPL TPP,14.351195,80.143086
+SHIRPUR TPP,21.15103,74.854206
+SOUTHERN REPL. TPS,22.549591,88.291512
+TALWANDI SABO TPP,29.92458,75.238929
+TAMNAR TPP,22.098747,83.451312
+UCHPINDA TPP,21.8864,83.12081
+UTKAL TPP (IND BARATH),21.657942,83.919513
+UTRAULA TPS,27.43,82.18
+VEDANTA TPP,21.815743,84.042036
+VIZAG TPP,17.563642,83.138877
+WARDHA WARORA TPP,20.272443,78.978916
+SIKKA REP. TPS,22.420828,69.8276
+ADANI POWER LIMITED MUNDRA TPP - III,22.8234904,69.5534831
+ADANI POWER LIMITED MUNDRA TPP - I & II,22.8234904,69.5534831
+ADANI POWER LIMITED UDUPI TPP,13.1596,74.8002
+ITPCL TPP,11.5216092,79.7518447
+JSW RATNAGIRI TPP,17.296375,73.213105
+MUNDRA UMTPP,22.8158,69.5281
+MUTHIARA TPP,8.912716,78.144965
+OPG Power Generation Private Limited,13.437422,80.09788
+SALAYA TPP,22.3049,69.7101
+SHREE CEMENT LTD TPS,26.073889,74.385
+SIMHAPURI TPS,14.204576,80.086636
+THAMMINAPATNAM TPS,,
+TORANGALLU TPS(SBU-I),15.186771,76.6621387
+TORANGALLU TPS(SBU-II),15.186771,76.6621387
+TROMBAY TPS,19.0027,72.8979
+TUTICORIN TPP ST-IV,,
+BUTIBORI TPP,20.928591,78.933281
+MIHAN TPS,21.0377,78.9334
+NASIK (P) TPS,,
+SALORA TPP,22.489952,82.606239
+TUTICORIN (P) TPP,,
+TITAGARH TPS,22.731974,88.370309
+CHAKABURA TPP,22.393653,82.553249
+GEPL TPP Ph-I,19.92949,79.133159
+KASAIPALLI TPP,22.38895,82.5549
+KATGHORA TPP,22.489779,82.60618
+NIWARI TPP,25.43,79.71
+RATIJA TPS,22.339994,82.492776
+SVPL TPP,22.292,82.5254
+SWASTIK KORBA TPP,,

CoalTPP_Locations.geojson

@@ -0,0 +1,183 @@
+{
+"type": "FeatureCollection",
+"name": "CoalTPP_Locations",
+"crs": { "type": "name", "properties": { "name": "urn:ogc:def:crs:OGC:1.3:CRS84" } },
+"features": [
+{ "type": "Feature", "properties": { "Name": "PANIPAT TPS", "Latitude": 29.3975059, "Longitude": 76.8763161 }, "geometry": { "type": "Point", "coordinates": [ 76.8763161, 29.3975059 ] } },
+{ "type": "Feature", "properties": { "Name": "RAJIV GANDHI TPS", "Latitude": 29.34698, "Longitude": 75.857538 }, "geometry": { "type": "Point", "coordinates": [ 75.857538, 29.34698 ] } },
+{ "type": "Feature", "properties": { "Name": "YAMUNA NAGAR TPS", "Latitude": 30.1075175, "Longitude": 77.3267646 }, "geometry": { "type": "Point", "coordinates": [ 77.3267646, 30.1075175 ] } },
+{ "type": "Feature", "properties": { "Name": "GH TPS (LEH.MOH.)", "Latitude": 30.2674, "Longitude": 75.1644 }, "geometry": { "type": "Point", "coordinates": [ 75.1644, 30.2674 ] } },
+{ "type": "Feature", "properties": { "Name": "GOINDWAL SAHIB TPP", "Latitude": 31.38414, "Longitude": 75.146503 }, "geometry": { "type": "Point", "coordinates": [ 75.146503, 31.38414 ] } },
+{ "type": "Feature", "properties": { "Name": "ROPAR TPS", "Latitude": 31.0417, "Longitude": 76.5847 }, "geometry": { "type": "Point", "coordinates": [ 76.5847, 31.0417 ] } },
+{ "type": "Feature", "properties": { "Name": "CHHABRA-II TPP", "Latitude": 24.6236, "Longitude": 77.0383 }, "geometry": { "type": "Point", "coordinates": [ 77.0383, 24.6236 ] } },
+{ "type": "Feature", "properties": { "Name": "CHHABRA-I PH-1 TPP", "Latitude": 24.62092, "Longitude": 77.035439 }, "geometry": { "type": "Point", "coordinates": [ 77.035439, 24.62092 ] } },
+{ "type": "Feature", "properties": { "Name": "CHHABRA-I PH-2 TPP", "Latitude": 24.63433, "Longitude": 77.0385039 }, "geometry": { "type": "Point", "coordinates": [ 77.0385039, 24.63433 ] } },
+{ "type": "Feature", "properties": { "Name": "KALISINDH TPS", "Latitude": 24.5325129, "Longitude": 76.100353 }, "geometry": { "type": "Point", "coordinates": [ 76.100353, 24.5325129 ] } },
+{ "type": "Feature", "properties": { "Name": "KOTA TPS", "Latitude": 25.1717, "Longitude": 75.8169 }, "geometry": { "type": "Point", "coordinates": [ 75.8169, 25.1717 ] } },
+{ "type": "Feature", "properties": { "Name": "SURATGARH STPS", "Latitude": 29.181766, "Longitude": 74.018741 }, "geometry": { "type": "Point", "coordinates": [ 74.018741, 29.181766 ] } },
+{ "type": "Feature", "properties": { "Name": "SURATGARH TPS", "Latitude": 29.181766, "Longitude": 74.018741 }, "geometry": { "type": "Point", "coordinates": [ 74.018741, 29.181766 ] } },
+{ "type": "Feature", "properties": { "Name": "ANPARA TPS", "Latitude": 24.202043, "Longitude": 82.789128 }, "geometry": { "type": "Point", "coordinates": [ 82.789128, 24.202043 ] } },
+{ "type": "Feature", "properties": { "Name": "HARDUAGANJ TPS", "Latitude": 28.0175, "Longitude": 78.13 }, "geometry": { "type": "Point", "coordinates": [ 78.13, 28.0175 ] } },
+{ "type": "Feature", "properties": { "Name": "JAWAHARPUR STPP", "Latitude": 27.497098, "Longitude": 78.826071 }, "geometry": { "type": "Point", "coordinates": [ 78.826071, 27.497098 ] } },
+{ "type": "Feature", "properties": { "Name": "OBRA TPS", "Latitude": 24.4445803, "Longitude": 82.9803325 }, "geometry": { "type": "Point", "coordinates": [ 82.9803325, 24.4445803 ] } },
+{ "type": "Feature", "properties": { "Name": "PARICHHA TPS", "Latitude": 25.513571, "Longitude": 78.757918 }, "geometry": { "type": "Point", "coordinates": [ 78.757918, 25.513571 ] } },
+{ "type": "Feature", "properties": { "Name": "DSPM TPS", "Latitude": 22.368196, "Longitude": 82.721986 }, "geometry": { "type": "Point", "coordinates": [ 82.721986, 22.368196 ] } },
+{ "type": "Feature", "properties": { "Name": "KORBA-WEST TPS", "Latitude": 22.412536, "Longitude": 82.682187 }, "geometry": { "type": "Point", "coordinates": [ 82.682187, 22.412536 ] } },
+{ "type": "Feature", "properties": { "Name": "MARWA TPS", "Latitude": 22.070801, "Longitude": 82.602189 }, "geometry": { "type": "Point", "coordinates": [ 82.602189, 22.070801 ] } },
+{ "type": "Feature", "properties": { "Name": "GANDHI NAGAR TPS", "Latitude": 23.249722, "Longitude": 72.673889 }, "geometry": { "type": "Point", "coordinates": [ 72.673889, 23.249722 ] } },
+{ "type": "Feature", "properties": { "Name": "UKAI TPS", "Latitude": 21.2093, "Longitude": 73.5574 }, "geometry": { "type": "Point", "coordinates": [ 73.5574, 21.2093 ] } },
+{ "type": "Feature", "properties": { "Name": "WANAKBORI TPS", "Latitude": 22.8725375, "Longitude": 73.3588028 }, "geometry": { "type": "Point", "coordinates": [ 73.3588028, 22.8725375 ] } },
+{ "type": "Feature", "properties": { "Name": "AMARKANTAK EXT TPS", "Latitude": 23.162146, "Longitude": 81.632816 }, "geometry": { "type": "Point", "coordinates": [ 81.632816, 23.162146 ] } },
+{ "type": "Feature", "properties": { "Name": "SANJAY GANDHI TPS", "Latitude": 23.305744, "Longitude": 81.064817 }, "geometry": { "type": "Point", "coordinates": [ 81.064817, 23.305744 ] } },
+{ "type": "Feature", "properties": { "Name": "SATPURA TPS", "Latitude": 22.108133, "Longitude": 78.177072 }, "geometry": { "type": "Point", "coordinates": [ 78.177072, 22.108133 ] } },
+{ "type": "Feature", "properties": { "Name": "SHREE SINGAJI TPP", "Latitude": 22.09914, "Longitude": 76.531727 }, "geometry": { "type": "Point", "coordinates": [ 76.531727, 22.09914 ] } },
+{ "type": "Feature", "properties": { "Name": "BHUSAWAL TPS", "Latitude": 21.04798, "Longitude": 75.85502 }, "geometry": { "type": "Point", "coordinates": [ 75.85502, 21.04798 ] } },
+{ "type": "Feature", "properties": { "Name": "CHANDRAPUR(MAHARASHTRA) STPS", "Latitude": 20.0063, "Longitude": 79.29 }, "geometry": { "type": "Point", "coordinates": [ 79.29, 20.0063 ] } },
+{ "type": "Feature", "properties": { "Name": "KHAPARKHEDA TPS", "Latitude": 21.276138, "Longitude": 79.121518 }, "geometry": { "type": "Point", "coordinates": [ 79.121518, 21.276138 ] } },
+{ "type": "Feature", "properties": { "Name": "KORADI TPS", "Latitude": 21.2420685, "Longitude": 79.0949659 }, "geometry": { "type": "Point", "coordinates": [ 79.0949659, 21.2420685 ] } },
+{ "type": "Feature", "properties": { "Name": "NASIK TPS", "Latitude": 19.9814589, "Longitude": 73.8920556 }, "geometry": { "type": "Point", "coordinates": [ 73.8920556, 19.9814589 ] } },
+{ "type": "Feature", "properties": { "Name": "PARAS TPS", "Latitude": 20.7147, "Longitude": 76.7948 }, "geometry": { "type": "Point", "coordinates": [ 76.7948, 20.7147 ] } },
+{ "type": "Feature", "properties": { "Name": "PARLI TPS", "Latitude": 18.9075857, "Longitude": 76.5429497 }, "geometry": { "type": "Point", "coordinates": [ 76.5429497, 18.9075857 ] } },
+{ "type": "Feature", "properties": { "Name": "Dr. N.TATA RAO TPS", "Latitude": 16.5984901, "Longitude": 80.5365853 }, "geometry": { "type": "Point", "coordinates": [ 80.5365853, 16.5984901 ] } },
+{ "type": "Feature", "properties": { "Name": "RAYALASEEMA TPS", "Latitude": 14.697282, "Longitude": 78.459364 }, "geometry": { "type": "Point", "coordinates": [ 78.459364, 14.697282 ] } },
+{ "type": "Feature", "properties": { "Name": "DAMODARAM SANJEEVAIAH TPS", "Latitude": 14.327604, "Longitude": 80.122105 }, "geometry": { "type": "Point", "coordinates": [ 80.122105, 14.327604 ] } },
+{ "type": "Feature", "properties": { "Name": "BELLARY TPS", "Latitude": 15.1931738, "Longitude": 76.7195034 }, "geometry": { "type": "Point", "coordinates": [ 76.7195034, 15.1931738 ] } },
+{ "type": "Feature", "properties": { "Name": "RAICHUR TPS", "Latitude": 16.3532, "Longitude": 77.3422 }, "geometry": { "type": "Point", "coordinates": [ 77.3422, 16.3532 ] } },
+{ "type": "Feature", "properties": { "Name": "YERMARUS TPP", "Latitude": 16.295261, "Longitude": 77.35612 }, "geometry": { "type": "Point", "coordinates": [ 77.35612, 16.295261 ] } },
+{ "type": "Feature", "properties": { "Name": "METTUR TPS", "Latitude": 11.7720201, "Longitude": 77.8141046 }, "geometry": { "type": "Point", "coordinates": [ 77.8141046, 11.7720201 ] } },
+{ "type": "Feature", "properties": { "Name": "METTUR TPS - II", "Latitude": 11.7744564, "Longitude": 77.8172243 }, "geometry": { "type": "Point", "coordinates": [ 77.8172243, 11.7744564 ] } },
+{ "type": "Feature", "properties": { "Name": "NORTH CHENNAI TPS", "Latitude": 13.253013, "Longitude": 80.327804 }, "geometry": { "type": "Point", "coordinates": [ 80.327804, 13.253013 ] } },
+{ "type": "Feature", "properties": { "Name": "TUTICORIN TPS", "Latitude": 8.762518, "Longitude": 78.175976 }, "geometry": { "type": "Point", "coordinates": [ 78.175976, 8.762518 ] } },
+{ "type": "Feature", "properties": { "Name": "SINGARENI TPP", "Latitude": 18.837231, "Longitude": 79.574766 }, "geometry": { "type": "Point", "coordinates": [ 79.574766, 18.837231 ] } },
+{ "type": "Feature", "properties": { "Name": "BHADRADRI TPP", "Latitude": 18.027488, "Longitude": 80.735645 }, "geometry": { "type": "Point", "coordinates": [ 80.735645, 18.027488 ] } },
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+{ "type": "Feature", "properties": { "Name": "MUTHIARA TPP", "Latitude": 8.912716, "Longitude": 78.144965 }, "geometry": { "type": "Point", "coordinates": [ 78.144965, 8.912716 ] } },
+{ "type": "Feature", "properties": { "Name": "OPG Power Generation Private Limited", "Latitude": 13.437422, "Longitude": 80.09788 }, "geometry": { "type": "Point", "coordinates": [ 80.09788, 13.437422 ] } },
+{ "type": "Feature", "properties": { "Name": "SALAYA TPP", "Latitude": 22.3049, "Longitude": 69.7101 }, "geometry": { "type": "Point", "coordinates": [ 69.7101, 22.3049 ] } },
+{ "type": "Feature", "properties": { "Name": "SHREE CEMENT LTD TPS", "Latitude": 26.073889, "Longitude": 74.385 }, "geometry": { "type": "Point", "coordinates": [ 74.385, 26.073889 ] } },
+{ "type": "Feature", "properties": { "Name": "SIMHAPURI TPS", "Latitude": 14.204576, "Longitude": 80.086636 }, "geometry": { "type": "Point", "coordinates": [ 80.086636, 14.204576 ] } },
+{ "type": "Feature", "properties": { "Name": "TORANGALLU TPS(SBU-I)", "Latitude": 15.186771, "Longitude": 76.6621387 }, "geometry": { "type": "Point", "coordinates": [ 76.6621387, 15.186771 ] } },
+{ "type": "Feature", "properties": { "Name": "TORANGALLU TPS(SBU-II)", "Latitude": 15.186771, "Longitude": 76.6621387 }, "geometry": { "type": "Point", "coordinates": [ 76.6621387, 15.186771 ] } },
+{ "type": "Feature", "properties": { "Name": "TROMBAY TPS", "Latitude": 19.0027, "Longitude": 72.8979 }, "geometry": { "type": "Point", "coordinates": [ 72.8979, 19.0027 ] } },
+{ "type": "Feature", "properties": { "Name": "BUTIBORI TPP", "Latitude": 20.928591, "Longitude": 78.933281 }, "geometry": { "type": "Point", "coordinates": [ 78.933281, 20.928591 ] } },
+{ "type": "Feature", "properties": { "Name": "MIHAN TPS", "Latitude": 21.0377, "Longitude": 78.9334 }, "geometry": { "type": "Point", "coordinates": [ 78.9334, 21.0377 ] } },
+{ "type": "Feature", "properties": { "Name": "SALORA TPP", "Latitude": 22.489952, "Longitude": 82.606239 }, "geometry": { "type": "Point", "coordinates": [ 82.606239, 22.489952 ] } },
+{ "type": "Feature", "properties": { "Name": "TITAGARH TPS", "Latitude": 22.731974, "Longitude": 88.370309 }, "geometry": { "type": "Point", "coordinates": [ 88.370309, 22.731974 ] } },
+{ "type": "Feature", "properties": { "Name": "CHAKABURA TPP", "Latitude": 22.393653, "Longitude": 82.553249 }, "geometry": { "type": "Point", "coordinates": [ 82.553249, 22.393653 ] } },
+{ "type": "Feature", "properties": { "Name": "GEPL TPP Ph-I", "Latitude": 19.92949, "Longitude": 79.133159 }, "geometry": { "type": "Point", "coordinates": [ 79.133159, 19.92949 ] } },
+{ "type": "Feature", "properties": { "Name": "KASAIPALLI TPP", "Latitude": 22.38895, "Longitude": 82.5549 }, "geometry": { "type": "Point", "coordinates": [ 82.5549, 22.38895 ] } },
+{ "type": "Feature", "properties": { "Name": "KATGHORA TPP", "Latitude": 22.489779, "Longitude": 82.60618 }, "geometry": { "type": "Point", "coordinates": [ 82.60618, 22.489779 ] } },
+{ "type": "Feature", "properties": { "Name": "NIWARI TPP", "Latitude": 25.43, "Longitude": 79.71 }, "geometry": { "type": "Point", "coordinates": [ 79.71, 25.43 ] } },
+{ "type": "Feature", "properties": { "Name": "RATIJA TPS", "Latitude": 22.339994, "Longitude": 82.492776 }, "geometry": { "type": "Point", "coordinates": [ 82.492776, 22.339994 ] } },
+{ "type": "Feature", "properties": { "Name": "SVPL TPP", "Latitude": 22.292, "Longitude": 82.5254 }, "geometry": { "type": "Point", "coordinates": [ 82.5254, 22.292 ] } }
+]
+}

app.py

@@ -0,0 +1,739 @@
+import streamlit as st
+import json
+import ee
+import os
+import pandas as pd
+import geopandas as gpd
+from datetime import datetime
+import leafmap.foliumap as leafmap
+import re
+from shapely.geometry import base
+from xml.etree import ElementTree as XET
+from concurrent.futures import ThreadPoolExecutor, as_completed
+import time
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+# Set up the page layout
+st.set_page_config(layout="wide")
+
+# Custom button styling
+m = st.markdown(
+    """
+    <style>
+    div.stButton > button:first-child {
+        background-color: #006400;
+        color:#ffffff;
+    }
+    </style>""",
+    unsafe_allow_html=True,
+)
+
+# Logo and Title
+st.write(
+    f"""
+    <div style="display: flex; justify-content: space-between; align-items: center;">
+        <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/ISRO_Logo.png" style="width: 20%; margin-right: auto;">
+        <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/SAC_Logo.png" style="width: 20%; margin-left: auto;">
+    </div>
+    """,
+    unsafe_allow_html=True,
+)
+st.markdown(
+    f"""
+    <div style="display: flex; flex-direction: column; align-items: center;">
+        <img src="https://huggingface.co/spaces/YashMK89/GEE_Calculator/resolve/main/SATRANG.png" style="width: 30%;">
+        <h3 style="text-align: center; margin: 0;">( Spatial and Temporal Aggregation for Remote-sensing Analysis of GEE Data )</h3>
+    </div>
+    <hr>
+    """,
+    unsafe_allow_html=True,
+)
+
+# Authenticate and initialize Earth Engine
+earthengine_credentials = os.environ.get("EE_Authentication")
+os.makedirs(os.path.expanduser("~/.config/earthengine/"), exist_ok=True)
+with open(os.path.expanduser("~/.config/earthengine/credentials"), "w") as f:
+    f.write(earthengine_credentials)
+ee.Initialize(project='ee-yashsacisro24')
+
+# Helper function to get reducer
+def get_reducer(reducer_name):
+    reducers = {
+        'mean': ee.Reducer.mean(),
+        'sum': ee.Reducer.sum(),
+        'median': ee.Reducer.median(),
+        'min': ee.Reducer.min(),
+        'max': ee.Reducer.max(),
+        'count': ee.Reducer.count(),
+    }
+    return reducers.get(reducer_name.lower(), ee.Reducer.mean())
+
+# Function to convert geometry to Earth Engine format
+def convert_to_ee_geometry(geometry):
+    if isinstance(geometry, base.BaseGeometry):
+        if geometry.is_valid:
+            geojson = geometry.__geo_interface__
+            return ee.Geometry(geojson)
+        else:
+            raise ValueError("Invalid geometry: The polygon geometry is not valid.")
+    elif isinstance(geometry, dict) or isinstance(geometry, str):
+        try:
+            if isinstance(geometry, str):
+                geometry = json.loads(geometry)
+            if 'type' in geometry and 'coordinates' in geometry:
+                return ee.Geometry(geometry)
+            else:
+                raise ValueError("GeoJSON format is invalid.")
+        except Exception as e:
+            raise ValueError(f"Error parsing GeoJSON: {e}")
+    elif isinstance(geometry, str) and geometry.lower().endswith(".kml"):
+        try:
+            tree = XET.parse(geometry)
+            kml_root = tree.getroot()
+            kml_namespace = {'kml': 'http://www.opengis.net/kml/2.2'}
+            coordinates = kml_root.findall(".//kml:coordinates", kml_namespace)
+            if coordinates:
+                coords_text = coordinates[0].text.strip()
+                coords = coords_text.split()
+                coords = [tuple(map(float, coord.split(','))) for coord in coords]
+                geojson = {"type": "Polygon", "coordinates": [coords]}
+                return ee.Geometry(geojson)
+            else:
+                raise ValueError("KML does not contain valid coordinates.")
+        except Exception as e:
+            raise ValueError(f"Error parsing KML: {e}")
+    else:
+        raise ValueError("Unsupported geometry input type. Supported types are Shapely, GeoJSON, and KML.")
+
+# Function to calculate custom formula
+def calculate_custom_formula(image, geometry, selected_bands, custom_formula, reducer_choice, dataset_id, user_scale=None):
+    try:
+        # Determine the scale: Use user-defined scale if provided, otherwise use dataset's native resolution
+        default_scale = ee.ImageCollection(dataset_id).first().select(0).projection().nominalScale().getInfo()
+        scale = user_scale if user_scale is not None else default_scale
+        band_values = {}
+        band_names = image.bandNames().getInfo()
+        for band in selected_bands:
+            if band not in band_names:
+                raise ValueError(f"Band '{band}' not found in the dataset.")
+            band_values[band] = image.select(band)
+        reducer = get_reducer(reducer_choice)
+        reduced_values = {}
+        for band in selected_bands:
+            value = band_values[band].reduceRegion(
+                reducer=reducer,
+                geometry=geometry,
+                scale=scale
+            ).get(band).getInfo()
+            reduced_values[band] = float(value if value is not None else 0)
+        formula = custom_formula
+        for band in selected_bands:
+            formula = formula.replace(band, str(reduced_values[band]))
+        result = eval(formula, {"__builtins__": {}}, reduced_values)
+        if not isinstance(result, (int, float)):
+            raise ValueError("Formula did not result in a numeric value.")
+        return ee.Image.constant(result).rename('custom_result')
+    except ZeroDivisionError:
+        st.error("Error: Division by zero in the formula.")
+        return ee.Image(0).rename('custom_result').set('error', 'Division by zero')
+    except SyntaxError:
+        st.error(f"Error: Invalid syntax in formula '{custom_formula}'.")
+        return ee.Image(0).rename('custom_result').set('error', 'Invalid syntax')
+    except ValueError as e:
+        st.error(f"Error: {str(e)}")
+        return ee.Image(0).rename('custom_result').set('error', str(e))
+    except Exception as e:
+        st.error(f"Unexpected error: {e}")
+        return ee.Image(0).rename('custom_result').set('error', str(e))
+
+# Aggregation functions
+def aggregate_data_custom(collection):
+    collection = collection.map(lambda image: image.set('day', ee.Date(image.get('system:time_start')).format('YYYY-MM-dd')))
+    grouped_by_day = collection.aggregate_array('day').distinct()
+    def calculate_daily_mean(day):
+        daily_collection = collection.filter(ee.Filter.eq('day', day))
+        daily_mean = daily_collection.mean()
+        return daily_mean.set('day', day)
+    daily_images = ee.List(grouped_by_day.map(calculate_daily_mean))
+    return ee.ImageCollection(daily_images)
+
+def aggregate_data_daily(collection):
+    def set_day_start(image):
+        date = ee.Date(image.get('system:time_start'))
+        day_start = date.format('YYYY-MM-dd')
+        return image.set('day_start', day_start)
+    collection = collection.map(set_day_start)
+    grouped_by_day = collection.aggregate_array('day_start').distinct()
+    def calculate_daily_mean(day_start):
+        daily_collection = collection.filter(ee.Filter.eq('day_start', day_start))
+        daily_mean = daily_collection.mean()
+        return daily_mean.set('day_start', day_start)
+    daily_images = ee.List(grouped_by_day.map(calculate_daily_mean))
+    return ee.ImageCollection(daily_images)
+
+def aggregate_data_weekly(collection, start_date_str, end_date_str):
+    start_date = ee.Date(start_date_str)
+    end_date = ee.Date(end_date_str)
+    days_diff = end_date.difference(start_date, 'day')
+    num_weeks = days_diff.divide(7).ceil().getInfo()
+    weekly_images = []
+    for week in range(num_weeks):
+        week_start = start_date.advance(week * 7, 'day')
+        week_end = week_start.advance(7, 'day')
+        weekly_collection = collection.filterDate(week_start, week_end)
+        if weekly_collection.size().getInfo() > 0:
+            weekly_mean = weekly_collection.mean()
+            weekly_mean = weekly_mean.set('week_start', week_start.format('YYYY-MM-dd'))
+            weekly_images.append(weekly_mean)
+    return ee.ImageCollection.fromImages(weekly_images)
+
+def aggregate_data_monthly(collection, start_date, end_date):
+    collection = collection.filterDate(start_date, end_date)
+    collection = collection.map(lambda image: image.set('month', ee.Date(image.get('system:time_start')).format('YYYY-MM')))
+    grouped_by_month = collection.aggregate_array('month').distinct()
+    def calculate_monthly_mean(month):
+        monthly_collection = collection.filter(ee.Filter.eq('month', month))
+        monthly_mean = monthly_collection.mean()
+        return monthly_mean.set('month', month)
+    monthly_images = ee.List(grouped_by_month.map(calculate_monthly_mean))
+    return ee.ImageCollection(monthly_images)
+
+def aggregate_data_yearly(collection):
+    collection = collection.map(lambda image: image.set('year', ee.Date(image.get('system:time_start')).format('YYYY')))
+    grouped_by_year = collection.aggregate_array('year').distinct()
+    def calculate_yearly_mean(year):
+        yearly_collection = collection.filter(ee.Filter.eq('year', year))
+        yearly_mean = yearly_collection.mean()
+        return yearly_mean.set('year', year)
+    yearly_images = ee.List(grouped_by_year.map(calculate_yearly_mean))
+    return ee.ImageCollection(yearly_images)
+
+def preprocess_collection(collection, tile_cloud_threshold, pixel_cloud_threshold):
+    def filter_tile(image):
+        cloud_percentage = calculate_cloud_percentage(image, cloud_band='QA60')
+        return image.set('cloud_percentage', cloud_percentage).updateMask(cloud_percentage.lt(tile_cloud_threshold))
+    def mask_cloudy_pixels(image):
+        qa60 = image.select('QA60')
+        opaque_clouds = qa60.bitwiseAnd(1 << 10)
+        cirrus_clouds = qa60.bitwiseAnd(1 << 11)
+        cloud_mask = opaque_clouds.Or(cirrus_clouds)
+        clear_pixels = cloud_mask.Not()
+        return image.updateMask(clear_pixels)
+    filtered_collection = collection.map(filter_tile)
+    masked_collection = filtered_collection.map(mask_cloudy_pixels)
+    return masked_collection
+
+def process_single_geometry(row, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, custom_formula, original_lat_col, original_lon_col, kernel_size=None, include_boundary=None, user_scale=None):
+    if shape_type.lower() == "point":
+        latitude = row.get('latitude')
+        longitude = row.get('longitude')
+        if pd.isna(latitude) or pd.isna(longitude):
+            return None
+        location_name = row.get('name', f"Location_{row.name}")
+        if kernel_size == "3x3 Kernel":
+            buffer_size = 45
+            roi = ee.Geometry.Point([longitude, latitude]).buffer(buffer_size).bounds()
+        elif kernel_size == "5x5 Kernel":
+            buffer_size = 75
+            roi = ee.Geometry.Point([longitude, latitude]).buffer(buffer_size).bounds()
+        else:
+            roi = ee.Geometry.Point([longitude, latitude])
+    elif shape_type.lower() == "polygon":
+        polygon_geometry = row.get('geometry')
+        location_name = row.get('name', f"Polygon_{row.name}")
+        try:
+            roi = convert_to_ee_geometry(polygon_geometry)
+            if not include_boundary:
+                roi = roi.buffer(-30).bounds()
+        except ValueError:
+            return None
+    collection = ee.ImageCollection(dataset_id) \
+        .filterDate(ee.Date(start_date_str), ee.Date(end_date_str)) \
+        .filterBounds(roi)
+    if aggregation_period.lower() == 'custom (start date to end date)':
+        collection = aggregate_data_custom(collection)
+    elif aggregation_period.lower() == 'daily':
+        collection = aggregate_data_daily(collection)
+    elif aggregation_period.lower() == 'weekly':
+        collection = aggregate_data_weekly(collection, start_date_str, end_date_str)
+    elif aggregation_period.lower() == 'monthly':
+        collection = aggregate_data_monthly(collection, start_date_str, end_date_str)
+    elif aggregation_period.lower() == 'yearly':
+        collection = aggregate_data_yearly(collection)
+    image_list = collection.toList(collection.size())
+    processed_weeks = set()
+    aggregated_results = []
+    for i in range(image_list.size().getInfo()):
+        image = ee.Image(image_list.get(i))
+        if aggregation_period.lower() == 'custom (start date to end date)':
+            timestamp = image.get('day')
+            period_label = 'Date'
+            date = ee.Date(timestamp).format('YYYY-MM-dd').getInfo()
+        elif aggregation_period.lower() == 'daily':
+            timestamp = image.get('day_start')
+            period_label = 'Date'
+            date = ee.String(timestamp).getInfo()
+        elif aggregation_period.lower() == 'weekly':
+            timestamp = image.get('week_start')
+            period_label = 'Week'
+            date = ee.String(timestamp).getInfo()
+            if (pd.to_datetime(date) < pd.to_datetime(start_date_str) or 
+                pd.to_datetime(date) > pd.to_datetime(end_date_str) or 
+                date in processed_weeks):
+                continue
+            processed_weeks.add(date)
+        elif aggregation_period.lower() == 'monthly':
+            timestamp = image.get('month')
+            period_label = 'Month'
+            date = ee.Date(timestamp).format('YYYY-MM').getInfo()
+        elif aggregation_period.lower() == 'yearly':
+            timestamp = image.get('year')
+            period_label = 'Year'
+            date = ee.Date(timestamp).format('YYYY').getInfo()
+        index_image = calculate_custom_formula(image, roi, selected_bands, custom_formula, reducer_choice, dataset_id, user_scale=user_scale)
+        try:
+            index_value = index_image.reduceRegion(
+                reducer=get_reducer(reducer_choice),
+                geometry=roi,
+                scale=user_scale
+            ).get('custom_result')
+            calculated_value = index_value.getInfo()
+            if isinstance(calculated_value, (int, float)):
+                result = {
+                    'Location Name': location_name,
+                    period_label: date,
+                    'Start Date': start_date_str,
+                    'End Date': end_date_str,
+                    'Calculated Value': calculated_value
+                }
+                if shape_type.lower() == 'point':
+                    result[original_lat_col] = latitude
+                    result[original_lon_col] = longitude
+                aggregated_results.append(result)
+        except Exception as e:
+            st.error(f"Error retrieving value for {location_name}: {e}")
+    return aggregated_results
+
+def process_aggregation(locations_df, start_date_str, end_date_str, dataset_id, selected_bands, reducer_choice, shape_type, aggregation_period, original_lat_col, original_lon_col, custom_formula="", kernel_size=None, include_boundary=None, tile_cloud_threshold=0, pixel_cloud_threshold=0, user_scale=None):
+    aggregated_results = []
+    total_steps = len(locations_df)
+    progress_bar = st.progress(0)
+    progress_text = st.empty()
+    start_time = time.time()
+    raw_collection = ee.ImageCollection(dataset_id) \
+        .filterDate(ee.Date(start_date_str), ee.Date(end_date_str))
+    st.write(f"Original Collection Size: {raw_collection.size().getInfo()}")
+    if tile_cloud_threshold > 0 or pixel_cloud_threshold > 0:
+        raw_collection = preprocess_collection(raw_collection, tile_cloud_threshold, pixel_cloud_threshold)
+        st.write(f"Preprocessed Collection Size: {raw_collection.size().getInfo()}")
+    with ThreadPoolExecutor(max_workers=10) as executor:
+        futures = []
+        for idx, row in locations_df.iterrows():
+            future = executor.submit(
+                process_single_geometry,
+                row,
+                start_date_str,
+                end_date_str,
+                dataset_id,
+                selected_bands,
+                reducer_choice,
+                shape_type,
+                aggregation_period,
+                custom_formula,
+                original_lat_col,
+                original_lon_col,
+                kernel_size,
+                include_boundary,
+                user_scale=user_scale
+            )
+            futures.append(future)
+        completed = 0
+        for future in as_completed(futures):
+            result = future.result()
+            if result:
+                aggregated_results.extend(result)
+            completed += 1
+            progress_percentage = completed / total_steps
+            progress_bar.progress(progress_percentage)
+            progress_text.markdown(f"Processing: {int(progress_percentage * 100)}%")
+    end_time = time.time()
+    processing_time = end_time - start_time
+    if aggregated_results:
+        result_df = pd.DataFrame(aggregated_results)
+        if aggregation_period.lower() == 'custom (start date to end date)':
+            agg_dict = {
+                'Start Date': 'first',
+                'End Date': 'first',
+                'Calculated Value': 'mean'
+            }
+            if shape_type.lower() == 'point':
+                agg_dict[original_lat_col] = 'first'
+                agg_dict[original_lon_col] = 'first'
+            aggregated_output = result_df.groupby('Location Name').agg(agg_dict).reset_index()
+            aggregated_output['Date Range'] = aggregated_output['Start Date'] + " to " + aggregated_output['End Date']
+            aggregated_output.rename(columns={'Calculated Value': 'Aggregated Value'}, inplace=True)
+            return aggregated_output.to_dict(orient='records'), processing_time
+        else:
+            return result_df.to_dict(orient='records'), processing_time 
+    return [], processing_time  
+
+# Streamlit App Logic
+st.markdown("<h5>Image Collection</h5>", unsafe_allow_html=True)
+imagery_base = st.selectbox("Select Imagery Base", ["Sentinel", "Landsat", "MODIS", "VIIRS", "Custom Input"], index=0)
+data = {}
+if imagery_base == "Sentinel":
+    dataset_file = "sentinel_datasets.json"
+    try:
+        with open(dataset_file) as f:
+            data = json.load(f)
+    except FileNotFoundError:
+        st.error(f"Dataset file '{dataset_file}' not found.")
+        data = {}
+elif imagery_base == "Landsat":
+    dataset_file = "landsat_datasets.json"
+    try:
+        with open(dataset_file) as f:
+            data = json.load(f)
+    except FileNotFoundError:
+        st.error(f"Dataset file '{dataset_file}' not found.")
+        data = {}
+elif imagery_base == "MODIS":
+    dataset_file = "modis_datasets.json"
+    try:
+        with open(dataset_file) as f:
+            data = json.load(f)
+    except FileNotFoundError:
+        st.error(f"Dataset file '{dataset_file}' not found.")
+        data = {}
+elif imagery_base == "VIIRS":
+    dataset_file = "viirs_datasets.json"
+    try:
+        with open(dataset_file) as f:
+            data = json.load(f)
+    except FileNotFoundError:
+        st.error(f"Dataset file '{dataset_file}' not found.")
+        data = {}
+elif imagery_base == "Custom Input":
+    custom_dataset_id = st.text_input("Enter Custom Earth Engine Dataset ID (e.g., AHN/AHN4)", value="")
+    if custom_dataset_id:
+        try:
+            if custom_dataset_id.startswith("ee.ImageCollection("):
+                custom_dataset_id = custom_dataset_id.replace("ee.ImageCollection('", "").replace("')", "")
+            collection = ee.ImageCollection(custom_dataset_id)
+            band_names = collection.first().bandNames().getInfo()
+            data = {
+                f"Custom Dataset: {custom_dataset_id}": {
+                    "sub_options": {custom_dataset_id: f"Custom Dataset ({custom_dataset_id})"},
+                    "bands": {custom_dataset_id: band_names}
+                }
+            }
+            st.write(f"Fetched bands for {custom_dataset_id}: {', '.join(band_names)}")
+        except Exception as e:
+            st.error(f"Error fetching dataset: {str(e)}. Please check the dataset ID and ensure it's valid in Google Earth Engine.")
+            data = {}
+    else:
+        st.warning("Please enter a custom dataset ID to proceed.")
+        data = {}
+if not data:
+    st.error("No valid dataset available. Please check your inputs.")
+    st.stop()
+
+st.markdown("<hr><h5><b>{}</b></h5>".format(imagery_base), unsafe_allow_html=True)
+main_selection = st.selectbox(f"Select {imagery_base} Dataset Category", list(data.keys()))
+sub_selection = None
+dataset_id = None
+if main_selection:
+    sub_options = data[main_selection]["sub_options"]
+    sub_selection = st.selectbox(f"Select Specific {imagery_base} Dataset ID", list(sub_options.keys()))
+    if sub_selection:
+        st.write(f"You selected: {main_selection} -> {sub_options[sub_selection]}")
+        st.write(f"Dataset ID: {sub_selection}")
+        dataset_id = sub_selection
+
+st.markdown("<hr><h5><b>Earth Engine Index Calculator</b></h5>", unsafe_allow_html=True)
+if main_selection and sub_selection:
+    dataset_bands = data[main_selection]["bands"].get(sub_selection, [])
+    st.write(f"Available Bands for {sub_options[sub_selection]}: {', '.join(dataset_bands)}")
+    selected_bands = st.multiselect(
+        "Select 1 or 2 Bands for Calculation",
+        options=dataset_bands,
+        default=[dataset_bands[0]] if dataset_bands else [],
+        help=f"Select 1 or 2 bands from: {', '.join(dataset_bands)}"
+    )
+    if len(selected_bands) < 1:
+        st.warning("Please select at least one band.")
+        st.stop()
+    if selected_bands:
+        if len(selected_bands) == 1:
+            default_formula = f"{selected_bands[0]}"
+            example = f"'{selected_bands[0]} * 2' or '{selected_bands[0]} + 1'"
+        else:
+            default_formula = f"({selected_bands[0]} - {selected_bands[1]}) / ({selected_bands[0]} + {selected_bands[1]})"
+            example = f"'{selected_bands[0]} * {selected_bands[1]} / 2' or '({selected_bands[0]} - {selected_bands[1]}) / ({selected_bands[0]} + {selected_bands[1]})'"
+        custom_formula = st.text_input(
+            "Enter Custom Formula (e.g (B8 - B4) / (B8 + B4) , B4*B3/2)",
+            value=default_formula,
+            help=f"Use only these bands: {', '.join(selected_bands)}. Examples: {example}"
+        )
+        def validate_formula(formula, selected_bands):
+            allowed_chars = set(" +-*/()0123456789.")
+            terms = re.findall(r'[a-zA-Z][a-zA-Z0-9_]*', formula)
+            invalid_terms = [term for term in terms if term not in selected_bands]
+            if invalid_terms:
+                return False, f"Invalid terms in formula: {', '.join(invalid_terms)}. Use only {', '.join(selected_bands)}."
+            if not all(char in allowed_chars or char in ''.join(selected_bands) for char in formula):
+                return False, "Formula contains invalid characters. Use only bands, numbers, and operators (+, -, *, /, ())"
+            return True, ""
+        is_valid, error_message = validate_formula(custom_formula, selected_bands)
+        if not is_valid:
+            st.error(error_message)
+            st.stop()
+        elif not custom_formula:
+            st.warning("Please enter a custom formula to proceed.")
+            st.stop()
+        st.write(f"Custom Formula: {custom_formula}")
+
+reducer_choice = st.selectbox(
+    "Select Reducer (e.g, mean , sum , median , min , max , count)",
+    ['mean', 'sum', 'median', 'min', 'max', 'count'],
+    index=0
+)
+start_date = st.date_input("Start Date", value=pd.to_datetime('2024-11-01'))
+end_date = st.date_input("End Date", value=pd.to_datetime('2024-12-01'))
+start_date_str = start_date.strftime('%Y-%m-%d')
+end_date_str = end_date.strftime('%Y-%m-%d')
+
+if imagery_base == "Sentinel" and "Sentinel-2" in sub_options[sub_selection]:
+    st.markdown("<h5>Cloud Filtering</h5>", unsafe_allow_html=True)
+    tile_cloud_threshold = st.slider(
+        "Select Maximum Tile-Based Cloud Coverage Threshold (%)",
+        min_value=0,
+        max_value=100,
+        value=20,
+        step=5,
+        help="Tiles with cloud coverage exceeding this threshold will be excluded."
+    )
+    pixel_cloud_threshold = st.slider(
+        "Select Maximum Pixel-Based Cloud Coverage Threshold (%)",
+        min_value=0,
+        max_value=100,
+        value=10,
+        step=5,
+        help="Individual pixels with cloud coverage exceeding this threshold will be masked."
+    )
+
+aggregation_period = st.selectbox(
+    "Select Aggregation Period (e.g, Custom(Start Date to End Date) , Daily , Weekly , Monthly , Yearly)",
+    ["Custom (Start Date to End Date)", "Daily", "Weekly", "Monthly", "Yearly"],
+    index=0
+)
+shape_type = st.selectbox("Do you want to process 'Point' or 'Polygon' data?", ["Point", "Polygon"])
+kernel_size = None
+include_boundary = None
+if shape_type.lower() == "point":
+    kernel_size = st.selectbox(
+        "Select Calculation Area(e.g, Point , 3x3 Kernel , 5x5 Kernel)",
+        ["Point", "3x3 Kernel", "5x5 Kernel"],
+        index=0,
+        help="Choose 'Point' for exact point calculation, or a kernel size for area averaging."
+    )
+elif shape_type.lower() == "polygon":
+    include_boundary = st.checkbox(
+        "Include Boundary Pixels",
+        value=True,
+        help="Check to include pixels on the polygon boundary; uncheck to exclude them."
+    )
+
+st.markdown("<h5>Calculation Scale</h5>", unsafe_allow_html=True)
+default_scale = ee.ImageCollection(dataset_id).first().select(0).projection().nominalScale().getInfo()
+user_scale = st.number_input(
+    "Enter Calculation Scale (meters) [Leave blank to use dataset's default scale]",
+    min_value=1.0,
+    value=float(default_scale),
+    help=f"Default scale for this dataset is {default_scale} meters. Adjust if needed."
+)
+
+file_upload = st.file_uploader(f"Upload your {shape_type} data (CSV, GeoJSON, KML)", type=["csv", "geojson", "kml"])
+locations_df = pd.DataFrame()
+original_lat_col = None
+original_lon_col = None
+if file_upload is not None:
+    if shape_type.lower() == "point":
+        if file_upload.name.endswith('.csv'):
+            locations_df = pd.read_csv(file_upload)
+            st.write("Preview of your uploaded data (first 5 rows):")
+            st.dataframe(locations_df.head())
+            all_columns = locations_df.columns.tolist()
+            col1, col2 = st.columns(2)
+            with col1:
+                original_lat_col = st.selectbox(
+                    "Select Latitude Column",
+                    options=all_columns,
+                    index=all_columns.index('latitude') if 'latitude' in all_columns else 0,
+                    help="Select the column containing latitude values"
+                )
+            with col2:
+                original_lon_col = st.selectbox(
+                    "Select Longitude Column",
+                    options=all_columns,
+                    index=all_columns.index('longitude') if 'longitude' in all_columns else 0,
+                    help="Select the column containing longitude values"
+                )
+            if not pd.api.types.is_numeric_dtype(locations_df[original_lat_col]) or not pd.api.types.is_numeric_dtype(locations_df[original_lon_col]):
+                st.error("Error: Selected Latitude and Longitude columns must contain numeric values")
+                st.stop()
+            locations_df = locations_df.rename(columns={
+                original_lat_col: 'latitude',
+                original_lon_col: 'longitude'
+            })
+        elif file_upload.name.endswith('.geojson'):
+            locations_df = gpd.read_file(file_upload)
+            if 'geometry' in locations_df.columns:
+                locations_df['latitude'] = locations_df['geometry'].y
+                locations_df['longitude'] = locations_df['geometry'].x
+                original_lat_col = 'latitude'
+                original_lon_col = 'longitude'
+            else:
+                st.error("GeoJSON file doesn't contain geometry column")
+                st.stop()
+        elif file_upload.name.endswith('.kml'):
+            kml_string = file_upload.read().decode('utf-8')
+            try:
+                root = XET.fromstring(kml_string)
+                ns = {'kml': 'http://www.opengis.net/kml/2.2'}
+                points = []
+                for placemark in root.findall('.//kml:Placemark', ns):
+                    name = placemark.findtext('kml:name', default=f"Point_{len(points)}", namespaces=ns)
+                    coords_elem = placemark.find('.//kml:Point/kml:coordinates', ns)
+                    if coords_elem is not None:
+                        coords_text = coords_elem.text.strip()
+                        coords = [c.strip() for c in coords_text.split(',')]
+                        if len(coords) >= 2:
+                            lon, lat = float(coords[0]), float(coords[1])
+                            points.append({'name': name, 'geometry': f"POINT ({lon} {lat})"})
+                if not points:
+                    st.error("No valid Point data found in the KML file.")
+                else:
+                    locations_df = gpd.GeoDataFrame(points, geometry=gpd.GeoSeries.from_wkt([p['geometry'] for p in points]), crs="EPSG:4326")
+                    locations_df['latitude'] = locations_df['geometry'].y
+                    locations_df['longitude'] = locations_df['geometry'].x
+                    original_lat_col = 'latitude'
+                    original_lon_col = 'longitude'
+            except Exception as e:
+                st.error(f"Error parsing KML file: {str(e)}")
+        if not locations_df.empty and 'latitude' in locations_df.columns and 'longitude' in locations_df.columns:
+            m = leafmap.Map(center=[locations_df['latitude'].mean(), locations_df['longitude'].mean()], zoom=10)
+            for _, row in locations_df.iterrows():
+                latitude = row['latitude']
+                longitude = row['longitude']
+                if pd.isna(latitude) or pd.isna(longitude):
+                    continue
+                m.add_marker(location=[latitude, longitude], popup=row.get('name', 'No Name'))
+            st.write("Map of Uploaded Points:")
+            m.to_streamlit()
+    elif shape_type.lower() == "polygon":
+        if file_upload.name.endswith('.csv'):
+            st.error("CSV upload not supported for polygons. Please upload a GeoJSON or KML file.")
+        elif file_upload.name.endswith('.geojson'):
+            locations_df = gpd.read_file(file_upload)
+            if 'geometry' not in locations_df.columns:
+                st.error("GeoJSON file doesn't contain geometry column")
+                st.stop()
+        elif file_upload.name.endswith('.kml'):
+            kml_string = file_upload.read().decode('utf-8')
+            try:
+                root = XET.fromstring(kml_string)
+                ns = {'kml': 'http://www.opengis.net/kml/2.2'}
+                polygons = []
+                for placemark in root.findall('.//kml:Placemark', ns):
+                    name = placemark.findtext('kml:name', default=f"Polygon_{len(polygons)}", namespaces=ns)
+                    coords_elem = placemark.find('.//kml:Polygon//kml:coordinates', ns)
+                    if coords_elem is not None:
+                        coords_text = ' '.join(coords_elem.text.split())
+                        coord_pairs = [pair.split(',')[:2] for pair in coords_text.split() if pair]
+                        if len(coord_pairs) >= 4:
+                            coords_str = " ".join([f"{float(lon)} {float(lat)}" for lon, lat in coord_pairs])
+                            polygons.append({'name': name, 'geometry': f"POLYGON (({coords_str}))"})
+                if not polygons:
+                    st.error("No valid Polygon data found in the KML file.")
+                else:
+                    locations_df = gpd.GeoDataFrame(polygons, geometry=gpd.GeoSeries.from_wkt([p['geometry'] for p in polygons]), crs="EPSG:4326")
+            except Exception as e:
+                st.error(f"Error parsing KML file: {str(e)}")
+        if not locations_df.empty and 'geometry' in locations_df.columns:
+            centroid_lat = locations_df.geometry.centroid.y.mean()
+            centroid_lon = locations_df.geometry.centroid.x.mean()
+            m = leafmap.Map(center=[centroid_lat, centroid_lon], zoom=10)
+            for _, row in locations_df.iterrows():
+                polygon = row['geometry']
+                if polygon.is_valid:
+                    gdf = gpd.GeoDataFrame([row], geometry=[polygon], crs=locations_df.crs)
+                    m.add_gdf(gdf=gdf, layer_name=row.get('name', 'Unnamed Polygon'))
+            st.write("Map of Uploaded Polygons:")
+            m.to_streamlit()
+
+if st.button(f"Calculate {custom_formula}"):
+    if not locations_df.empty:
+        with st.spinner("Processing Data..."):
+            try:
+                results, processing_time = process_aggregation(
+                    locations_df,
+                    start_date_str,
+                    end_date_str,
+                    dataset_id,
+                    selected_bands,
+                    reducer_choice,
+                    shape_type,
+                    aggregation_period,
+                    original_lat_col,
+                    original_lon_col,
+                    custom_formula,
+                    kernel_size,
+                    include_boundary,
+                    tile_cloud_threshold=tile_cloud_threshold if "tile_cloud_threshold" in locals() else 0,
+                    pixel_cloud_threshold=pixel_cloud_threshold if "pixel_cloud_threshold" in locals() else 0,
+                    user_scale=user_scale
+                )
+                if results:
+                    result_df = pd.DataFrame(results)
+                    st.write(f"Processed Results Table ({aggregation_period}) for Formula: {custom_formula}")
+                    st.dataframe(result_df)
+                    filename = f"{main_selection}_{dataset_id}_{start_date.strftime('%Y%m%d')}_{end_date.strftime('%Y%m%d')}_{aggregation_period.lower()}.csv"
+                    st.download_button(
+                        label="Download results as CSV",
+                        data=result_df.to_csv(index=False).encode('utf-8'),
+                        file_name=filename,
+                        mime='text/csv'
+                    )
+                    st.success(f"Processing complete! Total processing time: {processing_time:.2f} seconds.")
+                    st.markdown("<h5>Graph Visualization</h5>", unsafe_allow_html=True)
+                    if aggregation_period.lower() == 'custom (start date to end date)':
+                        x_column = 'Date Range'  
+                    elif 'Date' in result_df.columns:
+                        x_column = 'Date'
+                    elif 'Week' in result_df.columns:
+                        x_column = 'Week'
+                    elif 'Month' in result_df.columns:
+                        x_column = 'Month'
+                    elif 'Year' in result_df.columns:
+                        x_column = 'Year'
+                    else:
+                        st.warning("No valid time column found for plotting.")
+                        st.stop()
+                    y_column = 'Calculated Value'
+                    fig = px.line(
+                        result_df,
+                        x=x_column,
+                        y=y_column,
+                        color='Location Name',
+                        title=f"{custom_formula} Over Time"
+                    )
+                    st.plotly_chart(fig)
+                else:
+                    st.warning("No results were generated. Check your inputs or formula.")
+                    st.info(f"Total processing time: {processing_time:.2f} seconds.")
+            except Exception as e:
+                st.error(f"An error occurred during processing: {str(e)}")
+    else:
+        st.warning("Please upload a valid file to proceed.")

landsat_datasets.json

@@ -0,0 +1,113 @@
+{
+    "Landsat 8 OLI/TIRS: Operational Land Imager and Thermal Infrared Sensor": {
+        "sub_options": {
+            "LANDSAT/LC08/C02/T1_L2": "Landsat 8 OLI/TIRS (Level-2, Surface Reflectance)",
+            "LANDSAT/LC08/C02/T1_TOA": "Landsat 8 OLI/TIRS (Top of Atmosphere Reflectance)"
+        },
+        "bands": {
+            "LANDSAT/LC08/C02/T1_L2": [
+                "SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B6", "SR_B7",
+                "ST_B10", "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LC08/C02/T1_TOA": [
+                "B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11",
+                "QA_PIXEL", "QA_RADSAT"
+            ]
+        }
+    },
+    "Landsat 7 ETM+: Enhanced Thematic Mapper Plus": {
+        "sub_options": {
+            "LANDSAT/LE07/C02/T1_L2": "Landsat 7 ETM+ (Level-2, Surface Reflectance)",
+            "LANDSAT/LE07/C02/T1_TOA": "Landsat 7 ETM+ (Top of Atmosphere Reflectance)"
+        },
+        "bands": {
+            "LANDSAT/LE07/C02/T1_L2": [
+                "SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B7",
+                "ST_B6", "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LE07/C02/T1_TOA": [
+                "B1", "B2", "B3", "B4", "B5", "B6_VCID_1", "B6_VCID_2", "B7", "B8",
+                "QA_PIXEL", "QA_RADSAT"
+            ]
+        }
+    },
+    "Landsat 5 TM: Thematic Mapper": {
+        "sub_options": {
+            "LANDSAT/LT05/C02/T1_L2": "Landsat 5 TM (Level-2, Surface Reflectance)",
+            "LANDSAT/LT05/C02/T1_TOA": "Landsat 5 TM (Top of Atmosphere Reflectance)"
+        },
+        "bands": {
+            "LANDSAT/LT05/C02/T1_L2": [
+                "SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B7",
+                "ST_B6", "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LT05/C02/T1_TOA": [
+                "B1", "B2", "B3", "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ]
+        }
+    },
+    "Landsat 9 OLI-2/TIRS-2: Operational Land Imager-2 and Thermal Infrared Sensor-2": {
+        "sub_options": {
+            "LANDSAT/LC09/C02/T1_L2": "Landsat 9 OLI-2/TIRS-2 (Level-2, Surface Reflectance)",
+            "LANDSAT/LC09/C02/T1_TOA": "Landsat 9 OLI-2/TIRS-2 (Top of Atmosphere Reflectance)"
+        },
+        "bands": {
+            "LANDSAT/LC09/C02/T1_L2": [
+                "SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B6", "SR_B7",
+                "ST_B10", "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LC09/C02/T1_TOA": [
+                "B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B9", "B10", "B11",
+                "QA_PIXEL", "QA_RADSAT"
+            ]
+        }
+    },
+    "Landsat 4 TM: Thematic Mapper": {
+        "sub_options": {
+            "LANDSAT/LT04/C02/T1_L2": "Landsat 4 TM (Level-2, Surface Reflectance)",
+            "LANDSAT/LT04/C02/T1_TOA": "Landsat 4 TM (Top of Atmosphere Reflectance)"
+        },
+        "bands": {
+            "LANDSAT/LT04/C02/T1_L2": [
+                "SR_B1", "SR_B2", "SR_B3", "SR_B4", "SR_B5", "SR_B7",
+                "ST_B6", "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LT04/C02/T1_TOA": [
+                "B1", "B2", "B3", "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ]
+        }
+    },
+    "Landsat 1-5 MSS: Multispectral Scanner": {
+        "sub_options": {
+            "LANDSAT/LM01/C02/T1": "Landsat 1 MSS (Level-1)",
+            "LANDSAT/LM02/C02/T1": "Landsat 2 MSS (Level-1)",
+            "LANDSAT/LM03/C02/T1": "Landsat 3 MSS (Level-1)",
+            "LANDSAT/LM04/C02/T1": "Landsat 4 MSS (Level-1)",
+            "LANDSAT/LM05/C02/T1": "Landsat 5 MSS (Level-1)"
+        },
+        "bands": {
+            "LANDSAT/LM01/C02/T1": [
+                "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LM02/C02/T1": [
+                "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LM03/C02/T1": [
+                "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LM04/C02/T1": [
+                "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ],
+            "LANDSAT/LM05/C02/T1": [
+                "B4", "B5", "B6", "B7",
+                "QA_PIXEL", "QA_RADSAT"
+            ]
+        }
+    }
+}

modis_datasets.json

@@ -0,0 +1,68 @@
+{
+    "MODIS Terra Surface Reflectance": {
+        "sub_options": {
+            "MODIS/061/MOD09GA": "MODIS Terra Surface Reflectance Daily Global 500m",
+            "MODIS/061/MOD09A1": "MODIS Terra Surface Reflectance 8-Day Global 500m"
+        },
+        "bands": {
+            "MODIS/061/MOD09GA": [
+                "sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b04",
+                "sur_refl_b05", "sur_refl_b06", "sur_refl_b07",
+                "QC_500m", "State_500m"
+            ],
+            "MODIS/061/MOD09A1": [
+                "sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b04",
+                "sur_refl_b05", "sur_refl_b06", "sur_refl_b07",
+                "QC_500m", "State_500m"
+            ]
+        }
+    },
+    "MODIS Aqua Surface Reflectance": {
+        "sub_options": {
+            "MODIS/061/MYD09GA": "MODIS Aqua Surface Reflectance Daily Global 500m",
+            "MODIS/061/MYD09A1": "MODIS Aqua Surface Reflectance 8-Day Global 500m"
+        },
+        "bands": {
+            "MODIS/061/MYD09GA": [
+                "sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b04",
+                "sur_refl_b05", "sur_refl_b06", "sur_refl_b07",
+                "QC_500m", "State_500m"
+            ],
+            "MODIS/061/MYD09A1": [
+                "sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b04",
+                "sur_refl_b05", "sur_refl_b06", "sur_refl_b07",
+                "QC_500m", "State_500m"
+            ]
+        }
+    },
+    "MODIS Terra Vegetation Indices": {
+        "sub_options": {
+            "MODIS/061/MOD13A1": "MODIS Terra Vegetation Indices 16-Day Global 500m",
+            "MODIS/061/MOD13Q1": "MODIS Terra Vegetation Indices 16-Day Global 250m"
+        },
+        "bands": {
+            "MODIS/061/MOD13A1": [
+                "NDVI", "EVI", "DetailedQA",
+                "sur_refl_b01", "sur_refl_b02", "sur_refl_b03", "sur_refl_b07"
+            ],
+            "MODIS/061/MOD13Q1": [
+                "NDVI", "EVI", "DetailedQA",
+                "sur_refl_b01", "sur_refl_b02"
+            ]
+        }
+    },
+    "MODIS Terra Land Surface Temperature": {
+        "sub_options": {
+            "MODIS/061/MOD11A1": "MODIS Terra Land Surface Temperature Daily Global 1km",
+            "MODIS/061/MOD11A2": "MODIS Terra Land Surface Temperature 8-Day Global 1km"
+        },
+        "bands": {
+            "MODIS/061/MOD11A1": [
+                "LST_Day_1km", "LST_Night_1km", "QC_Day", "QC_Night"
+            ],
+            "MODIS/061/MOD11A2": [
+                "LST_Day_1km", "LST_Night_1km", "QC_Day", "QC_Night"
+            ]
+        }
+    }
+}

requirements.txt

@@ -0,0 +1,8 @@
+streamlit
+ee
+geemap
+panda
+geopandas
+leafmap
+fastkml
+lxml

sentinel_datasets.json

@@ -0,0 +1,49 @@
+{
+    "Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar": {
+        "sub_options": {
+            "COPERNICUS/S1_GRD": "Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar"
+        },
+        "bands": {
+            "COPERNICUS/S1_GRD": ["VV", "VH", "HH", "HV"]
+        }
+    },
+    "Sentinel-2 MSI: Multispectral Instrument": {
+        "sub_options": {
+            "COPERNICUS/S2": "Sentinel-2 MSI: Multispectral Instrument (Level-1C)",
+            "COPERNICUS/S2_SR": "Sentinel-2 MSI: Multispectral Instrument (Level-2A)"
+        },
+        "bands": {
+            "COPERNICUS/S2": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B10", "B11", "B12"],
+            "COPERNICUS/S2_SR": ["B1", "B2", "B3", "B4", "B5", "B6", "B7", "B8", "B8A", "B9", "B11", "B12", "AOT", "WVP"]
+        }
+    },
+    "Sentinel-3 OLCI : Ocean and Land Color Instrument": {
+        "sub_options": {
+            "COPERNICUS/S3/OLCI": "Sentinel-3 OLCI : Ocean and Land Color Instrument"
+        },
+        "bands": {
+            "COPERNICUS/S3/OLCI": [
+                "Oa01_radiance", "Oa02_radiance", "Oa03_radiance", "Oa04_radiance", "Oa05_radiance",
+                "Oa06_radiance", "Oa07_radiance", "Oa08_radiance", "Oa09_radiance", "Oa10_radiance",
+                "Oa11_radiance", "Oa12_radiance", "Oa13_radiance", "Oa14_radiance", "Oa15_radiance",
+                "Oa16_radiance", "Oa17_radiance", "Oa18_radiance", "Oa19_radiance", "Oa20_radiance",
+                "Oa21_radiance"
+            ]
+        }
+    },
+    "Sentinel-5P TROPOMI: TROPOspheric Monitoring Instrument": {
+        "sub_options": {
+            "COPERNICUS/S5P/NRTI/L3_NO2": "Sentinel-5P TROPOMI: TROPOspheric Monitoring Instrument (NO2)",
+            "COPERNICUS/S5P/OFFL/L3_O3": "Sentinel-5P TROPOMI: TROPOspheric Monitoring Instrument (O3)"
+        },
+        "bands": {
+            "COPERNICUS/S5P/NRTI/L3_NO2": [
+                "NO2_column_number_density", "tropospheric_NO2_column_number_density", 
+                "stratospheric_NO2_column_number_density", "NO2_slant_column_number_density"
+            ],
+            "COPERNICUS/S5P/OFFL/L3_O3": [
+                "O3_column_number_density", "O3_effective_temperature"
+            ]
+        }
+    }
+}

viirs_datasets.json

@@ -0,0 +1,90 @@
+{
+    "VIIRS Day/Night Band Composites": {
+        "sub_options": {
+            "NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG": "VIIRS Day/Night Band Composite (Monthly V1)"
+        },
+        "bands": {
+            "NOAA/VIIRS/DNB/MONTHLY_V1/VCMSLCFG": [
+                "avg_rad", "cf_cvg", "cvg"
+            ]
+        }
+    },
+    "VIIRS Surface Reflectance": {
+        "sub_options": {
+            "NOAA/VIIRS/001/VNP09GA": "VIIRS/NPP Surface Reflectance 6-Min L2G Global 1km and 500m SIN Grid V001",
+            "NOAA/VIIRS/001/VJ109GA": "VIIRS/JPSS1 Surface Reflectance 6-Min L2G Global 1km and 500m SIN Grid V001"
+        },
+        "bands": {
+            "NOAA/VIIRS/001/VNP09GA": [
+                "M1", "M2", "M3", "M4", "M5", "M7", "M8", "M10", "M11",
+                "I1", "I2", "I3"
+            ],
+            "NOAA/VIIRS/001/VJ109GA": [
+                "M1", "M2", "M3", "M4", "M5", "M7", "M8", "M10", "M11",
+                "I1", "I2", "I3"
+            ]
+        }
+    },
+    "VIIRS Vegetation Indices": {
+        "sub_options": {
+            "NOAA/VIIRS/001/VNP13A1": "VIIRS/NPP Vegetation Indices 16-Day L3 Global 500m SIN Grid V001",
+            "NOAA/VIIRS/001/VJ113A1": "VIIRS/JPSS1 Vegetation Indices 16-Day L3 Global 500m SIN Grid V001"
+        },
+        "bands": {
+            "NOAA/VIIRS/001/VNP13A1": [
+                "NDVI", "EVI", "red", "nir", "blue", "MIR", "pixel_reliability",
+                "relative_azimuth_angle", "sun_zenith_angle", "satellite_zenith_angle"
+            ],
+            "NOAA/VIIRS/001/VJ113A1": [
+                "NDVI", "EVI", "red", "nir", "blue", "MIR", "pixel_reliability",
+                "relative_azimuth_angle", "sun_zenith_angle", "satellite_zenith_angle"
+            ]
+        }
+    },
+    "VIIRS Active Fire Detection": {
+        "sub_options": {
+            "NOAA/VIIRS/001/VNP14IMG": "VIIRS/NPP Thermal Anomalies/Fire 6-Min L2 Swath 375m V001",
+            "NOAA/VIIRS/001/VJ114IMG": "VIIRS/JPSS1 Thermal Anomalies/Fire 6-Min L2 Swath 375m V001"
+        },
+        "bands": {
+            "NOAA/VIIRS/001/VNP14IMG": [
+                "FireMask", "temperature", "confidence",
+                "brightness_temperature_3_7um", "brightness_temperature_11um"
+            ],
+            "NOAA/VIIRS/001/VJ114IMG": [
+                "FireMask", "temperature", "confidence",
+                "brightness_temperature_3_7um", "brightness_temperature_11um"
+            ]
+        }
+    },
+    "VIIRS Snow Cover": {
+        "sub_options": {
+            "NOAA/VIIRS/001/VNP10A1": "VIIRS/NPP Snow Cover 8-Day L3 Global 500m SIN Grid V001",
+            "NOAA/VIIRS/001/VJ110A1": "VIIRS/JPSS1 Snow Cover 8-Day L3 Global 500m SIN Grid V001"
+        },
+        "bands": {
+            "NOAA/VIIRS/001/VNP10A1": [
+                "NDSI_Snow_Cover", "Snow_Albedo_Daily_Tile",
+                "Snow_Albedo_Num_Obs_Daily_Tile", "Fractional_Snow_Cover",
+                "Snow_Albedo_Quality_Flag"
+            ],
+            "NOAA/VIIRS/001/VJ110A1": [
+                "NDSI_Snow_Cover", "Snow_Albedo_Daily_Tile",
+                "Snow_Albedo_Num_Obs_Daily_Tile", "Fractional_Snow_Cover",
+                "Snow_Albedo_Quality_Flag"
+            ]
+        }
+    },
+    "VIIRS Ocean Color/Chlorophyll": {
+        "sub_options": {
+            "NOAA/VIIRS/001/VNP09LOA": "VIIRS/NPP Ocean Color/Chlorophyll 8-Day L3 Global 4km SIN Grid V001"
+        },
+        "bands": {
+            "NOAA/VIIRS/001/VNP09LOA": [
+                "chlor_a", "chl_ocx", "Kd_490", "pic", "poc", "ipar", "nflh",
+                "par", "Rrs_412", "Rrs_443", "Rrs_488", "Rrs_555", "Rrs_678",
+                "Rrs_748", "Rrs_869"
+            ]
+        }
+    }
+}