SEMANTIC SEGMENTATION OF OIL-CONTAMINATED LAND FROM PLANETSCOPE IMAGERY USING NEURAL NETWORK MODELS

Abstract

Monitoring of oil-contaminated land is a pressing challenge for Kazakhstan. During the Soviet era, oil production at a number of fields relied on the so-called pit method, whereby drilling waste, process fluids, and associated wastewater were collected in purpose-dug earthen pits adjacent to extraction sites, leaving a substantial legacy of oil-contaminated areas requiring remediation and systematic monitoring. Remote sensing combined with deep learning-based image processing enables more accurate segmentation of such territories and facilitates their ongoing monitoring. This paper presents ROSID-HR, a dataset constructed from PlanetScope multispectral imagery designed for the segmentation of small-area oil contamination. In developing ROSID-HR, oil-contaminated sites from our previously published ROSID dataset were used as reference annotations, ensuring consistency between the two datasets. Through systematic experimentation, the optimal PlanetScope channel configuration for separating oil-contaminated areas from shadows and anthropogenic objects was identified as Blue-Red-NIR (bands 2-6-8). To validate the dataset, semantic segmentation was performed using the convolutional DeepLabv3+ model and the transformer-based Mask2Former architecture. Mask2Former achieved an IoU of 69.73% for the oil class on the test set, exceeding the DeepLabv3+ result of 21.63% by a factor of more than three. These results confirm the effectiveness of combining PlanetScope imagery with the Mask2Former architecture for the detection of small-area oil-contaminated sites.