Advances in machine learning research are pushing the limits of geographical information sciences (GIScience) by offering accurate procedures to analyze small-to-big GeoData. This Special Issue groups together six original contributions in the field of GeoData-driven GIScience that focus mainly on three different areas: extraction of semantic information from satellite imagery, image recommendation, and map generalization. Different technical approaches are chosen for each sub-topic, from deep learning to latent topic models
Substantial progress has been made toward developing effective techniques for spatial information pr...
We live in the era of ‘Big Data’. In particular, Geospatial data, whether captured through remote se...
Scholars in the humanities have long paid attention to spatial theory and cartographic outputs. More...
Advances in machine learning research are pushing the limits of geographical information sciences (G...
In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIS...
Recent years have seen a steady growth in the number of papers that apply machine learning methods t...
As computer and space technologies have been developed, geoscience information systems (GIS) and rem...
Recently, a need has arisen for prediction techniques that can address a variety of problems by comb...
Geoinformation derived from Earth observation satellite data is indispensable for many scientific, g...
Geospatial Computer Vision has become one of the most prevalent emerging fields of investigation in ...
This paper investigates recent research on active learning for (geo) text and image classification, ...
International audienceData is the central element of a geographic information system (GIS) and its c...
This manuscript presents an overview of my work in the field of geospatial machine learning, a rapid...
The article contains a survey of a Special Issue of the IEEE Geoscience and Remote Sensing Letters o...
Voluminous geographic data have been, and continue to be, collected with modern data acquisition tec...
Substantial progress has been made toward developing effective techniques for spatial information pr...
We live in the era of ‘Big Data’. In particular, Geospatial data, whether captured through remote se...
Scholars in the humanities have long paid attention to spatial theory and cartographic outputs. More...
Advances in machine learning research are pushing the limits of geographical information sciences (G...
In this paper GeoAI is introduced as an emergent spatial analytical framework for data-intensive GIS...
Recent years have seen a steady growth in the number of papers that apply machine learning methods t...
As computer and space technologies have been developed, geoscience information systems (GIS) and rem...
Recently, a need has arisen for prediction techniques that can address a variety of problems by comb...
Geoinformation derived from Earth observation satellite data is indispensable for many scientific, g...
Geospatial Computer Vision has become one of the most prevalent emerging fields of investigation in ...
This paper investigates recent research on active learning for (geo) text and image classification, ...
International audienceData is the central element of a geographic information system (GIS) and its c...
This manuscript presents an overview of my work in the field of geospatial machine learning, a rapid...
The article contains a survey of a Special Issue of the IEEE Geoscience and Remote Sensing Letters o...
Voluminous geographic data have been, and continue to be, collected with modern data acquisition tec...
Substantial progress has been made toward developing effective techniques for spatial information pr...
We live in the era of ‘Big Data’. In particular, Geospatial data, whether captured through remote se...
Scholars in the humanities have long paid attention to spatial theory and cartographic outputs. More...