Update README.md

README.md

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 * It covers five types of natural disasters and two types of man-made disasters across 14 regions worldwide, with a particular focus on developing countries.
 * About 4,200 paired optical and SAR images containing over 380,000 building instances in BRIGHT, with a spatial resolution between 0.3 and 1 meters, provides detailed representations of individual buildings.  
   
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-**IEEE GRSS Data Fusion Contest 2025**  
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+**Benchmark for building damage assessment**
+* Please download **pre-event.zip**, **post-event.zip**, and **target.zip**. Note that for the optical pre-event data in Ukraine, Myanmar, and Mexico, please follow our [instructions/tutorials](https://github.com/ChenHongruixuan/BRIGHT) to download. For the benchmark code and evaluation protocal for supervised building damage assessment, cross-event transfer, and unsupervised multimodal change detection, please see our [Github repo](https://github.com/ChenHongruixuan/BRIGHT).  
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+**Unsupervised multimodal image matching**  
+* BRIGHT supports the evaluation of Unsupervised Multimodal Image Matching (UMIM) algorithms for their performance in large-scale disaster scenarios. Please download data with the prefix "**umim**", such as **umim_noto_earthquake.zip**, and use our [code](https://github.com/ChenHongruixuan/BRIGHT) to test the exsiting algorithms' performance.  
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+**IEEE GRSS Data Fusion Contest 2025 (Closed, All Data Available)**  
 * BRIGHT also serves as the official dataset of [IEEE GRSS DFC 2025 Track II](https://www.grss-ieee.org/technical-committees/image-analysis-and-data-fusion/).  
 * Please download **dfc25_track2_trainval.zip** and unzip it. It contains training images & labels and validation images for the development phase.
 * Please download **dfc25_track2_test.zip** and unzip it. It contains test images for the final test phase.
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 * Benchmark code related to the DFC 2025 can be found at this [Github repo](https://github.com/ChenHongruixuan/BRIGHT).    
 * The official leaderboard is located on the [Codalab-DFC2025-Track II](https://codalab.lisn.upsaclay.fr/competitions/21122) page.
 
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-**Unsupervised multimodal image matching**  
-* BRIGHT supports the evaluation of Unsupervised Multimodal Image Matching (UMIM) algorithms for their performance in large-scale disaster scenarios. Please download data with the prefix "**umim**", such as **umim_noto_earthquake.zip**, and use our [code](https://github.com/ChenHongruixuan/BRIGHT) to test the exsiting algorithms' performance. 
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 **Paper & Reference**