Update README.md

README.md

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+---
+### 🔍 Users
+
+The following works used our AGBD dataset:
+* [Reconstructing Multi-Decadal Forest Disturbances: A Spatio-Temporal Transformer Approach](https://arxiv.org/abs/2606.07249)
+* [Biomazon: A Multimodal Dataset for 3D Forest Structure and Biomass Modeling in the Amazon Basin](https://arxiv.org/abs/2606.05368)
+* [Neural Processes Maintain Calibrated Biomass Estimates Across Spatiotemporal Gaps and Disturbance](https://arxiv.org/abs/2604.03874)
+* [MMEarth-Bench: Global Model Adaptation via Multimodal Test-Time Training](https://arxiv.org/abs/2602.06285)
+* [FLAIR-HUB: Large-scale multimodal dataset for land cover and crop mapping](https://www.sciencedirect.com/science/article/pii/S0924271626001899)
+* [LiDAR remote sensing meets weak supervision: Concepts, methods, and perspectives](https://www.sciencedirect.com/science/article/pii/S0924271626001152)
+* [MT-GSR4B: Multi-Temporally Guided Super-Resolution for Above-Ground Biomass Estimation](https://link.springer.com/article/10.1007/s41064-025-00370-x)
+* [Deep neural network-based approaches for aboveground biomass mapping in Indian Sundarbans](https://link.springer.com/article/10.1007/s41324-026-00676-x)
+* [Deep learning for estimation of bio- & geophysical parameters from SAR data](https://www.sciencedirect.com/science/chapter/edited-volume/pii/B9780443363443000180)
+* [The Calibration Gap: Accuracy Constraints of GEDI-Based Biomass Models in Heterogeneous Coffee Agroforestry in Mozambique](https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6050510)
+* [Accounting for 10 m Resolution Mapping for Above-Ground Biomass of Urban Trees in C40 Cities Across Eurasia Continent](https://www.mdpi.com/2072-4292/17/23/3898)
+* [Learning to Predict Aboveground Biomass from RGB Images with 3D Synthetic Scenes](https://arxiv.org/abs/2511.23249)
+* [Towards Unified Vision Language Models for Forest Ecological Analysis in Earth Observation](https://arxiv.org/abs/2511.16853)
+* [NeuCo-Bench: A Novel Benchmark Framework for Neural Embeddings in Earth Observation](https://arxiv.org/abs/2510.17914)
+* [Estimation of Forest Aboveground Biomass in China Based on GEDI and Sentinel-2 Data](https://www.mdpi.com/2072-4292/17/20/3437)
+* [Validating remotely sensed biomass estimates with forest inventory data in the western US](https://arxiv.org/abs/2506.03120)
+* [Unified deep learning model for global prediction of aboveground biomass, canopy height, and cover from high-resolution, multi-sensor satellite imagery](https://www.mdpi.com/2072-4292/17/9/1594)
+* [Above ground biomass mapping and validation across four biomes: U-NET based Sentinel-1, 2 and GEDI data combination](https://upcommons.upc.edu/entities/publication/19eb69cb-4382-406d-8445-2cda8bc6837f)
+* [SSL4Eco: A Global Seasonal Dataset for Geospatial Foundation Models in Ecology](https://openaccess.thecvf.com/content/CVPR2025W/EarthVision/html/Plekhanova_SSL4Eco_A_Global_Seasonal_Dataset_for_Geospatial_Foundation_Models_in_CVPRW_2025_paper.html)
+* [High-resolution aboveground biomass mapping: the benefits of biome-specific deep learning models](https://www.mdpi.com/2072-4292/17/7/1268)
+* [GSR4B: Biomass Map Super-Resolution with Sentinel-1/2 Guidance](https://arxiv.org/abs/2504.01722)
+* [When Machine Learning Meets Geospatial Data: A Comprehensive GeoAI Review](https://ieeexplore.ieee.org/abstract/document/10994795/)
+* [Advancements in Vision–Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques](https://www.mdpi.com/2072-4292/17/1/162)
+* [REO-VLM: Transforming VLM to Meet Regression Challenges in Earth Observation](https://arxiv.org/abs/2412.16583)