Update README.md
Browse files
README.md
CHANGED
@@ -52,4 +52,16 @@ If you use the DFC2020 dataset in your work, please cite the original paper:
|
|
52 |
pages={3185-3199},
|
53 |
keywords={Earth;Data integration;Remote sensing;Satellites;Training;Tensors;Synthetic aperture radar;Convolutional neural networks (CNNs);deep learning;image analysis and data fusion;land-cover mapping;multimodal;random forests (RFs);weak supervision},
|
54 |
doi={10.1109/JSTARS.2021.3063849}}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
55 |
```
|
|
|
52 |
pages={3185-3199},
|
53 |
keywords={Earth;Data integration;Remote sensing;Satellites;Training;Tensors;Synthetic aperture radar;Convolutional neural networks (CNNs);deep learning;image analysis and data fusion;land-cover mapping;multimodal;random forests (RFs);weak supervision},
|
54 |
doi={10.1109/JSTARS.2021.3063849}}
|
55 |
+
```
|
56 |
+
and if you also find our benchmark useful, please consider citing our paper:
|
57 |
+
```
|
58 |
+
@misc{si2025scalablefoundationmodelmultimodal,
|
59 |
+
title={Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data},
|
60 |
+
author={Haozhe Si and Yuxuan Wan and Minh Do and Deepak Vasisht and Han Zhao and Hendrik F. Hamann},
|
61 |
+
year={2025},
|
62 |
+
eprint={2503.12843},
|
63 |
+
archivePrefix={arXiv},
|
64 |
+
primaryClass={cs.CV},
|
65 |
+
url={https://arxiv.org/abs/2503.12843},
|
66 |
+
}
|
67 |
```
|