Update README.md

README.md

@@ -1,3 +1,44 @@
----
-license: mit
----
+---
+license: mit
+---
+## PreGRES: A Large-Scale Geospatial Dataset Collection
+
+**PreGRES** is a large-scale structured collection of existing smaller-scale geospatial datasets, designed for fine-tuning vision-language models in remote sensing applications. It integrates multiple sources, each contributing to different aspects of geospatial data understanding.
+
+The datasets within PreGRES provide coverage across three major tasks:
+
+---
+
+### 1. Image Captioning
+- **NWPU-Captions** (Cheng et al., 2022)  
+- **RSICD** (Lu et al., 2017)  
+- **RSITMD** (Yuan et al., 2022b)  
+- **Sydney-Captions** (Qu et al., 2016)  
+- **UCM-Captions** (Qu et al., 2016)  
+
+These datasets contribute paired image-text data and contain long-form descriptions of top-down imagery across diverse geospatial environments, enhancing language supervision.
+
+---
+
+### 2. Visual Question Answering (VQA)
+- **RSVQA LR** and **RSVQA HR** (Lobry et al., 2020)  
+- **FloodNet** (Rahnemoonfar et al., 2021)  
+- **RSIVQA** (Zheng et al., 2021)  
+
+These datasets include structured question-answer pairs supporting reasoning over aerial and satellite images, covering tasks such as object identification, scene understanding, and disaster assessment.
+
+---
+
+### 3. Visual Grounding / Region-Level Captioning
+- **DIOR-RSVG** (Zhan et al., 2023): Paired text-image data for object localization and spatial reference resolution.  
+- **NWPU-RESISC45** (Cheng et al., 2017): Scene classification labels.
+
+---
+
+### Dataset Statistics
+- **Images**: 119,279  
+- **Question-Answer Pairs**: 1,204,993  
+
+PreGRES is used in the first-stage pre-training of the **LISAT** model, enabling general-purpose geospatial question answering.
+
+> For more details on dataset composition, see **Table C.9** in our [paper](https://arxiv.org/pdf/2505.02829).