Environmental and more generally geospatial information is now provided by crowdsourcing but also by public administrations in the context of the open data policies. Analyses of such data are still challenging, because of their heterogeneity (structural, semantic, spatial, and temporal) and because of the difficulty in choosing the "best" knowledge discovery process to apply, according to the needs of the experts in the field. Challenges about data science deal with creation, storage, search, sharing, modeling, analysis, and visualization of data, information, and knowledge. This special issue of the International Journal of Data Science and Analytics Environmental and Geospatial Data Analytics contains a collection of seven papers and provides high-quality research covering part of the challenges mentioned above, from a theoretical or experimental point of view. It includes extended papers from the EnGeoData sessions of DSAA 2015 and DSAA 2016 (IEEE International Conference on Data Science and Advanced Analytics).