Spatio-Temporal Transformers for Long-Term NDVI Forecasting
Dept. of Computer Science, Bar-Ilan University（巴伊兰大学计算机科学系）；Dept. of Computer Science, College of Law（法律学院计算机科学系）；Faculty of Civil and Environmental Engineering, Technion Israel Institute of Technology（技术学院环境与土木工程学院）
Long-term satellite image time series (SITS) analysis in heterogeneous landscapes faces significant challenges, particularly in Mediterranean regions where complex spatial patterns, seasonal variations, and multi-decade environmental changes interact across different scales. This paper presents the Spatio-Temporal Transformer for Long Term Forecasting (STT-LTF ), an extended framework that advances beyond purely temporal analysis to integrate spatial context modeling with temporal sequence prediction. STT-LTF processes multi-scale spatial patches alongside temporal sequences (up to 20 years) through a unified transformer architecture, capturing both local neighborhood relationships and regional climate influences. The framework employs comprehensive self-supervised learning with spatial masking, temporal masking, and horizon sampling strategies, enabling robust model training from 40 years of unlabeled Landsat imagery. Unlike autoregressive approaches, STT-LTF directly predicts arbitrary future time points without error accumulation, incorporating spatial patch embeddings, cyclical temporal encoding, and geographic coordinates to learn complex dependencies across heterogeneous Mediterranean ecosystems. Experimental evaluation on Landsat data (1984-2024) demonstrates that STT-LTF achieves a Mean Absolute Error (MAE) of 0.0328 and R^2 of 0.8412 for next-year predictions, outperforming traditional statistical methods, CNN-based approaches, LSTM networks, and standard transformers. The framework's ability to handle irregular temporal sampling and variable prediction horizons makes it particularly suitable for analysis of heterogeneous landscapes experiencing rapid ecological transitions.
GeoMAE: Masking Representation Learning for Spatio-Temporal Graph Forecasting with Missing Values
College of Computer and Data Science, Fuzhou University（福州大学计算机与数据科学学院）；JD Intelligent Cities Research（京东智能城市研究院）；School of Computing and Artificial Intelligence, Southwest Jiaotong University（西南交通大学计算机与人工智能学院）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Beijing Normal University（北京师范大学）
Learning to forecast vegetation greenness at fine resolution over Africa with ConvLSTMs
Max-Planck-Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；ELLIS Unit Jena（耶拿欧洲学习与智能系统实验室）；Michael-Stifel-Center Jena for Data-driven and Simulation Science（耶拿米夏埃尔·施蒂费尔数据驱动与模拟科学中心）
