Sort by
Refine Your Search
-
such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
-
geospatial or remotely sensed data. Quantifying uncertainty and correcting for spatial and sampling biases inherent in environmental observation systems. Target environmental properties such as above ground
-
geospatial workflows on an abstract level, using purpose-driven concepts and conceptual transformations; develop AI and machine learning based technology to automate the description and modeling of data
-
supported approaches to help users formulate and translate natural language questions into structured representations that can be linked to geospatial data sources and workflows. You will contribute