[HTML][HTML] Geomorphometry and terrain analysis: Data, methods, platforms and applications
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 …
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
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 …
spatial analytics in Geography. Although much progress has been made in exploring the …
Csp: Self-supervised contrastive spatial pre-training for geospatial-visual representations
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 …
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
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 …
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?
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 …
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.
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) …
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
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 …
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
Abstract Information extraction is a key activity for remote sensing images. A common
distinction exists between knowledge-driven and data-driven methods. Knowledge-driven …
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
Research on geospatial foundation models (GFMs) has become a trending topic in
geospatial artificial intelligence (AI) research due to their potential for achieving high …
geospatial artificial intelligence (AI) research due to their potential for achieving high …
Towards general-purpose representation learning of polygonal geometries
Neural network representation learning for spatial data (eg, points, polylines, polygons, and
networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …
networks) is a common need for geographic artificial intelligence (GeoAI) problems. In …