[HTML][HTML] Geomorphometry and terrain analysis: Data, methods, platforms and applications

L **ong, S Li, G Tang, J Strobl - Earth-Science Reviews, 2022 - Elsevier
Terrain is considered one of the most essential natural geographic features and is a key
factor in physical processes. Geomorphometry and terrain analyses have provided a wealth …

GeoAI for large-scale image analysis and machine vision: recent progress of artificial intelligence in geography

W Li, CY Hsu - ISPRS International Journal of Geo-Information, 2022 - mdpi.com
GeoAI, or geospatial artificial intelligence, has become a trending topic and the frontier for
spatial analytics in Geography. Although much progress has been made in exploring the …

Csp: Self-supervised contrastive spatial pre-training for geospatial-visual representations

G Mai, N Lao, Y He, J Song… - … Conference on Machine …, 2023 - proceedings.mlr.press
Geo-tagged images are publicly available in large quantities, whereas labels such as object
classes are rather scarce and expensive to collect. Meanwhile, contrastive learning has …

Sphere2Vec: A general-purpose location representation learning over a spherical surface for large-scale geospatial predictions

G Mai, Y Xuan, W Zuo, Y He, J Song, S Ermon… - ISPRS Journal of …, 2023 - Elsevier
Generating learning-friendly representations for points in space is a fundamental and long-
standing problem in machine learning. Recently, multi-scale encoding schemes (such as …

Sustainability of floodplain wetland fisheries of rural Indonesia: does culture enhance livelihood resilience?

AS Hidayat, I Rajiani, D Arisanty - Sustainability, 2022 - mdpi.com
Preserving small-scale fisheries is the main concern of governments in sustainable growth
development because more than 90% of fishers and workers make a living in this business …

[PDF][PDF] Symbolic and subsymbolic GeoAI: Geospatial knowledge graphs and spatially explicit machine learning.

G Mai, Y Hu, S Gao, L Cai, B Martins, J Scholz… - Trans …, 2022 - geography.wisc.edu
The field of Artificial Intelligence (AI) can be roughly divided into two branches: Symbolic
Artificial Intelligence and Connectionist Artificial Intelligence (or so-called Subsymbolic AI) …

Explainable GeoAI: can saliency maps help interpret artificial intelligence's learning process? An empirical study on natural feature detection

CY Hsu, W Li - International Journal of Geographical Information …, 2023 - Taylor & Francis
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 learning. This …

[HTML][HTML] Geoscience-aware deep learning: A new paradigm for remote sensing

Y Ge, X Zhang, PM Atkinson, A Stein, L Li - Science of Remote Sensing, 2022 - Elsevier
Abstract Information extraction is a key activity for remote sensing images. A common
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …

Geospatial foundation models for image analysis: Evaluating and enhancing NASA-IBM Prithvi's domain adaptability

CY Hsu, W Li, S Wang - International Journal of Geographical …, 2024 - Taylor & Francis
Research on geospatial foundation models (GFMs) has become a trending topic in
geospatial artificial intelligence (AI) research due to their potential for achieving high …

Towards general-purpose representation learning of polygonal geometries

G Mai, C Jiang, W Sun, R Zhu, Y Xuan, L Cai… - GeoInformatica, 2023 - Springer
Neural network representation learning for spatial data (eg, points, polylines, polygons, and
networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …