Our study area included 20 areas of interest) from 6 countries.

SkySat satellite images were acquired through Planet Inc. Planet Explorer Catalogue. Obtained as a SkySat Collect product, each image was roughly 20km x 5.9km, with a spatial resolution ranging between 0.5m and 0.8m. Before download, all images were orthorectified, radiometrically calibrated, and atmospherically corrected to surface reflectance following Planet’s standard procedures. All surface reflectance products are orthorectified using fine digital elevation maps (30-90m posting) and ground control points. Planet conducts atmospheric correction with the 6SV2.1 radiative transfer model which accounts for atmospheric absorption and scattering, with aerosol optical depth, water, vapor and ozone inputs from MODIS near-real-time data (MOD09CMA and MOD09CMG). All calculations were done in R. Satellite images cover a time interval from March 2019 to June 2021. The satellite images can not be shared due to file size issues and data-sharing policies, below are the Skysat unique identification numbers for each satellite image used in the project:

20201107_025354_ssc9_u0001
20190510_152255_ssc4_u0003
20190625_182659_ssc7_u0001
20190309_183114_ssc11_u0001
20190531_182931_ssc6_u0001
20200919_182915_ssc7_u0002
20190626_182402_ssc8_u0003
20201129_191646_ssc8_u0001
20201022_191415_ssc6_u0001
20201022_191415_ssc6_u0002
20200626_192104_ssc6_u0001
20200626_192104_ssc6_u0002
20200627_190037_ssc7_u0001
20200627_191435_ssc8_u0001
20190413_081243_ssc13d3_0004
20190413_081243_ssc13d3_0005
20190413_081243_ssc13d3_0006
20200202_110140_ssc7_u0001
20210627_034355_ssc19_u0001
20210127_154738_ssc12_u0001

All non-overlapping halos in the AOIs were labeled, resulting in 4,127 manually annotated halos. Halos were labeled using ArcGIS software (ver. 2.9.1), allowing the geo-referenced information for all objects to be retained. To avoid biases due to a single user's perception of halos size, five users were trained to the same labeling procedure – zooming into each halo at a 1:600 scale and tracing light contours with a mouse – and labeled the same AOIs. The dataset generated from the imagery annotation was divided into training and test sets (~70% and ~30%, respectively). The training set was used for model implementation and optimization, while the test set was used for comparing model-predicted vs. manually annotated halos. In addition, we selected independent areas (i.e., AOIs) where no halos were used for the training process) for the test set to estimate the model generalization properties better.

Simone Franceschini and Amelia C. Meier downloaded the satellite images used for this project. Halos data were labeled by Simone Franceschini, Amelia C. Meier, Aviv Suan, Kaci Stokes, and Elizabeth M.P. Madin. Simone Franceschini developed the model and estimated performance metrics.