36 citations · 170 across the 34 of their papers we have counts for
18 papers · 1 filter
Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping
Hyunho Lee, Wenwen Li
The increasing number of satellites has improved the temporal resolution of Earth observation, making satellite-based flood mapping a promising approach for operational flood monit…
A Spatially Masked Adaptive Gated Network for multimodal post-flood water extent mapping using SAR and incomplete multispectral data
Hyunho Lee, Wenwen Li
Mapping water extent during a flood event is essential for effective disaster management throughout all phases: mitigation, preparedness, response, and recovery. In particular, dur…
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…
Geospatial Artificial Intelligence for Satellite-Based Flood Extent Mapping: Concepts, Advances, and Future Perspectives
Hyunho Lee, Wenwen Li
Geospatial Artificial Intelligence (GeoAI) for satellite-based flood extent mapping systematically integrates artificial intelligence techniques with satellite data to identify flo…
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…
Enhancing GeoAI and location encoding with spatial point pattern statistics: A Case Study of Terrain Feature Classification
Sizhe Wang, Wenwen Li
This study introduces a novel approach to terrain feature classification by incorporating spatial point pattern statistics into deep learning models. Inspired by the concept of loc…
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…
Improving Interpretability of Deep Active Learning for Flood Inundation Mapping Through Class Ambiguity Indices Using Multi-spectral Satellite Imagery
Hyunho Lee, Wenwen Li
Flood inundation mapping is a critical task for responding to the increasing risk of flooding linked to global warming. Significant advancements of deep learning in recent years ha…
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…