Multi-temporal Satellite Imagery Panoptic Segmentation of Agricultural Land in Ukraine

Abstract

Remote sensing of the Earth using satellites helps analyze the Earth’s resources, monitor local land surface changes, and study global climate changes. In particular, farmland information helps farmers in decision-making, planning and increases productivity to achieve better agro-ecological conditions. In this work, we primarily focus on panoptic segmentation of agricultural land, a combination of two parts: 1) delineation of parcels (instance segmentation) and 2) classification of parcel crop type (semantic segmentation). Second, we explore how multi-temporal satellite imagery data compares to a single image query in segmentation performance. Third, we conduct experiments using the recent advances in Deep Learning and Computer Vision that improve the performance of such systems. Finally, we show the performance of the state-of-the-art panoptic segmentation algorithm on the agricultural land of Ukraine, where the farmland market has just opened.