3 papers
cs.CV2024
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…
cs.CV2023
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…
cs.CV2023
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…