Utilizing Geospatial Data for Assessing Energy Security: Mapping Small Solar Home Systems Using Unmanned Aerial Vehicles and Deep Learning

Abstract

Solar home systems (SHS), a cost-effective solution for rural communities far from the grid in developing countries, are small solar panels and associated equipment that provides power to a single household. A crucial resource for targeting further investment of public and private resources, as well as tracking the progress of universal electrification goals, is shared access to high-quality data on individual SHS installations including information such as location and power capacity. Though recent studies utilizing satellite imagery and machine learning to detect solar panels have emerged, they struggle to accurately locate many SHS due to limited image resolution (some small solar panels only occupy several pixels in satellite imagery). In this work, we explore the viability and cost-performance tradeoff of using automatic SHS detection on unmanned aerial vehicle (UAV) imagery as an alternative to satellite imagery. More specifically, we explore three questions: (i) what is the detection performance of SHS using drone imagery; (ii) how expensive is the drone data collection, compared to satellite imagery; and (iii) how well does drone-based SHS detection perform in real-world scenarios? To examine these questions, we collect and publicly-release a dataset of high-resolution drone imagery encompassing SHS imaged under a variety of real-world conditions and use this dataset and a dataset of imagery from Rwanda to evaluate the capabilities of deep learning models to recognize SHS, including those that are too small to be reliably recognized in satellite imagery. The results suggest that UAV imagery may be a viable alternative to identify very small SHS from perspectives of both detection accuracy and financial costs of data collection. UAV-based data collection may be a practical option for supporting electricity access planning strategies for achieving sustainable development goals and for monitoring the progress towards those goals.

Deep Learning Resolves Myovascular Dynamics in the Failing Human Heart

“…Two channel images and their corresponding ground truth masks were used to train a U-Net–based convolutional neural network (CNN), as described previously. 11 , 16…”

“…CardioCount is a U-Net–based deep learning model with a ResNet50 backbone that was previously utilized to identify solar panels from drone imagery. 11 CardioCount identifies nuclei from antibody-labeled fluorescent images and can colocalize nuclear objects from multiple image channels. Inspired by the DeepLearning4Mic project, the software can run on Google Colab’s cloud resources and score thousands of images with few local computational tools.…”

Eyes in the Sky: Drones Applications in the Built Environment under Climate Change Challenges

“…Combined thermal and visible imagery facilitates quantifying the cooling provided by green spaces [81]. UAV spectral data also enable the construction of 3D urban models distinguishing rooftop materials to target solar panel deployment [82]. Moreover, repeat multispectral surveys allow the monitoring of the efficacy of resilience strategies, such as cool roofs and stormwater-retention landscaping.…”

Multi-Resolution Segmentation of Solar Photovoltaic Systems Using Deep Learning

“…While some early work on this topic deal with local spatial areas and individual PV systems [6], others generate datasets for entire countries [7]. Currently, from detailed local to global observations, various studies using remote sensing data such as satellite data [8,9], aerial images from aircraft [10,11], and drone images [12] exist to obtain detection at different scales in the respective study areas.…”