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README.md

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-# Remote Sensing Dataset: Substation Dataset
-
-## Description
-
-The first dataset is curated by TransitionZero and sourced from publicly available data repositories, including OpenSreetMap (OSMF) and Copernicus Sentinel data. The dataset consists of Sentinel-2 images from 27k+ locations; the task is to segment power-substations, which appear in the majority of locations in the dataset. Most locations have 4-5 images taken at different timepoints (i.e., revisits) and each image is of dimension 228x228 pixels. Each image has 13 spectral bands and each band has been linearly interpolated to a spatial resolution of 10m. Lastly, there is one ground truth mask for each location.
-
-### Key Features
-
-- **Source:**  OpenSreetMap (OSMF) and Copernicus Sentinel data
-- **Resolution:** 10m per pixel
-- **Bands:** 13 Sentinel-2 Bands
-- **Size:** Approximately 70GB
-
-We utilize this dataset in this [project](https://arxiv.org/abs/2409.17363). In this work, we focus on an applied research question of relevance to climate change mitigation -- power substation segmentation -- that is representative of applied uses of pre-trained models more generally. Through extensive tests of different multi-temporal input schemes across diverse model architectures, we find that fusing representations from multiple revisits in the model latent space is superior to other methods of using revisits, including as a form of data augmentation. We also find that a SWIN Transformer-based architecture performs better than U-nets and ViT-based models. 
-
----
-license: apache-2.0
----
+---
+license: cc-by-4.0
+---
+# Remote Sensing Dataset: Substation Dataset
+
+## Description
+
+The first dataset is curated by TransitionZero and sourced from publicly available data repositories, including OpenSreetMap (OSMF) and Copernicus Sentinel data. The dataset consists of Sentinel-2 images from 27k+ locations; the task is to segment power-substations, which appear in the majority of locations in the dataset. Most locations have 4-5 images taken at different timepoints (i.e., revisits) and each image is of dimension 228x228 pixels. Each image has 13 spectral bands and each band has been linearly interpolated to a spatial resolution of 10m. Lastly, there is one ground truth mask for each location.
+
+### Key Features
+
+- **Source:**  OpenSreetMap (OSMF) and Copernicus Sentinel data
+- **Resolution:** 10m per pixel
+- **Bands:** 13 Sentinel-2 Bands
+- **Size:** Approximately 70GB
+
+We utilize this dataset in this [project](https://arxiv.org/abs/2409.17363). In this work, we focus on an applied research question of relevance to climate change mitigation -- power substation segmentation -- that is representative of applied uses of pre-trained models more generally. Through extensive tests of different multi-temporal input schemes across diverse model architectures, we find that fusing representations from multiple revisits in the model latent space is superior to other methods of using revisits, including as a form of data augmentation. We also find that a SWIN Transformer-based architecture performs better than U-nets and ViT-based models. 
+
+---
+license: apache-2.0
+---