36 citations · 62 across the 14 of their papers we have counts for
14 papers
Asynchronous Remote Sensing Time-Series Fusion for Cloud Removal and Anytime Reconstruction
Forouzan Fallah, Chia Yu Hsu, Wenwen Li +2
Frequent cloud cover severely limits the usability of Sentinel-2 (S2) optical time series for Earth surface monitoring. Sentinel-1 (S1) SAR provides all-weather complementary obser…
Landslide Hazard Mapping with Geospatial Foundation Models: Geographical Generalizability, Data Scarcity, and Band Adaptability
Wenwen Li, Sizhe Wang, Hyunho Lee +4
Landslides cause severe damage to lives, infrastructure, and the environment, making accurate and timely mapping essential for disaster preparedness and response. However, conventi…
Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model
Forouzan Fallah, Wenwen Li, Chia-Yu Hsu +3
High spatial resolution satellite imagery is critical for monitoring fine-scale Earth surface processes, but is often limited by cost and revisit time. This work studies cross-sens…
Tobler's First Law in GeoAI: A Spatially Explicit Deep Learning Model for Terrain Feature Detection Under Weak Supervision
Wenwen Li, Chia-Yu Hsu, Maosheng Hu
Recent interest in geospatial artificial intelligence (GeoAI) has fostered a wide range of applications using artificial intelligence (AI), especially deep learning, for geospatial…
Pan-Arctic Permafrost Landform and Human-built Infrastructure Feature Detection with Vision Transformers and Location Embeddings
Amal S. Perera, David Fernandez, Chandi Witharana +9
Accurate mapping of permafrost landforms, thaw disturbances, and human-built infrastructure at pan-Arctic scale using sub-meter satellite imagery is increasingly critical. Handling…
A multi-scale vision transformer-based multimodal GeoAI model for mapping Arctic permafrost thaw
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +4
Retrogressive Thaw Slumps (RTS) in Arctic regions are distinct permafrost landforms with significant environmental impacts. Mapping these RTS is crucial because their appearance se…
Prithvi-EO-2.0: A Versatile Multi-Temporal Foundation Model for Earth Observation Applications
Daniela Szwarcman, Sujit Roy, Paolo Fraccaro +33
This paper presents Prithvi-EO-2.0, a new geospatial foundation model that offers significant improvements over its predecessor, Prithvi-EO-1.0. Trained on 4.2 million global time…
Geospatial foundation models for image analysis: evaluating and enhancing NASA-IBM Prithvi's domain adaptability
Chia-Yu Hsu, Wenwen Li, Sizhe Wang
Research on geospatial foundation models (GFMs) has become a trending topic in geospatial artificial intelligence (AI) research due to their potential for achieving high generaliza…
GeoAI Reproducibility and Replicability: a computational and spatial perspective
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +1
GeoAI has emerged as an exciting interdisciplinary research area that combines spatial theories and data with cutting-edge AI models to address geospatial problems in a novel, data…
Segment Anything Model Can Not Segment Anything: Assessing AI Foundation Model's Generalizability in Permafrost Mapping
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +10
This paper assesses trending AI foundation models, especially emerging computer vision foundation models and their performance in natural landscape feature segmentation. While the…
Assessment of a new GeoAI foundation model for flood inundation mapping
Wenwen Li, Hyunho Lee, Sizhe Wang +2
Vision foundation models are a new frontier in Geospatial Artificial Intelligence (GeoAI), an interdisciplinary research area that applies and extends AI for geospatial problem sol…
Real-time GeoAI for High-resolution Mapping and Segmentation of Arctic Permafrost Features
Wenwen Li, Chia-Yu Hsu, Sizhe Wang +2
This paper introduces a real-time GeoAI workflow for large-scale image analysis and the segmentation of Arctic permafrost features at a fine-granularity. Very high-resolution (0.5m…
Explainable GeoAI: Can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection
Chia-Yu Hsu, Wenwen Li
Improving the interpretability of geospatial artificial intelligence (GeoAI) models has become critically important to open the "black box" of complex AI models, such as deep learn…
Learning from Counting: Leveraging Temporal Classification for Weakly Supervised Object Localization and Detection
Chia-Yu Hsu, Wenwen Li
This paper reports a new solution of leveraging temporal classification to support weakly supervised object detection (WSOD). Specifically, we introduce raster scan-order technique…