Towards Geographic Aware Neural Networks for Geospatial Vector Data: A Case Study on Land Use and Land Cover Classification
Marvin Mc Cutchan & Ioannis Giannopoulos
Remotely sensed imagery is a well-established data source for spatial predictions, however, it comes with some disadvantages. Here, we explore how another data source could be used for spatial predictions, namely, geospatial vector data, by looking at a specific case study: LULC classification. We show how vector data can be encoded for an artificial neural network, making it geographically aware and enable it to predict LULC classes. We use two different encoding schemes as well as two different artificial neural network architectures. Our results suggest that geospatial vector data can be used for LULC classification and that the type of encoding and artificial neural network plays a significant role.
CEUR Workshop Proceedings, CIKM 2021, 3052, 2021-11-01.