Automatic detection of charcoal kilns on Very High Resolution images with a computer vision approach in Somalia

Abstract

In Somalia’s drylands, charcoal production is a major driver of forest degradation enabled by civil conflict and institutional weakness. Up to now, the extent and exact location of charcoal production has usually been estimated by visually detecting charcoal kilns on Very High Resolution (VHR) images. Taking advantage of the availability of a dataset of charcoal kilns delineated on VHR images by the Food and Agriculture Organization (FAO) experts, we designed a computer vision (CV) approach to automatically identify charcoal kilns on VHR images. The methodology relies on a curated subset of the expert-labeled dataset and a collection of panchromatic and pan-sharpened multispectral Natural Color (RGB) VHR for the years 2018 and 2019. Kiln delineations paired with the VHR images are visually reviewed and used with a Faster R-CNN model, an object detection deep learning method. A two-stage methodology is used to train the best models for panchromatic and RGB images, respectively. The first stage uses a small number of high quality pairs to define the best parameters of the models while the second stage uses a larger set of pairs to fine tune the previous models. The results indicates that charcoal kilns are detected with a precision of 90% in panchromatic images and of 80% in RGB images. The models are then used to predict the presence of kilns over the available VHR images. Omission errors are prioritized over commission errors to mitigate the difficulty in detecting kilns in some specific situations. Comparison between the FAO expert kiln dataset and objects predicted by the CNN model is giving encouraging results. With a visual screening complementing the proposed workflow, CV can aid charcoal kiln monitoring in Somalia while alleviating manual work.