Performing machine learning (ML) and analyzing geospatial data are both hard problems requiring a lot of domain expertise. These limitations have historically meant that one needs to be an expert in both to perform even the most basic analyses, making advances in AI for overhead imagery difficult to achieve. We asked ourselves: is there anything we can do to reduce this barrier to entry, making it easier to apply machine learning methods to overhead imagery data? Enter Solaris, a new Python library for ML analysis of geospatial data.

Solaris builds upon SpaceNet’s previous tool suite, SpaceNetUtilities, along with several other CosmiQ projects like BASISS to provide an end-to-end pipeline for geospatial AI. Solaris provides well-documented Python APIs and simple command line tools to complete every step of a geospatial ML pipeline with ease, including:

• Tile raw imagery and vector labels into pieces compatible with ML
• Convert vector labels to ML-compatible pixel masks
• Train state-of-the-art deep learning models with three lines of Python code
• Segment objects of interest with machine learning models (including the SpaceNet winners’ models, with pre-trained weights and configs provided!)
• Georegister predictions and convert them to standardized geospatial data formats
• Score model performance against hand-labeled ground truth using the SpaceNet datasets

Extensive documentation and tutorials are available for Solaris on the documentation page and on GitHub. The open source codebase is available under an Apache 2.0 license.

A Software Package to Facilitate Time-Series Analysis of Satellite Imagery

Comet Time Series (CometTS) is a software package and tool that enables workflows for the analysis and visualization of a time series of satellite imagery for the data science and geographic science communities. The tool aims to enable population estimation research, land use change detection, or natural disaster monitoring using a range of data types. CometTS calculates relevant statistical quantities (e.g., measures of central tendency and variation) and provides a visualization of their changes over time. The package can help to serve as a rapid inspection workflow and analyze multiple regions of interest (ROI)’s in seconds.

CometTS has been employed to monitor electrical and infrastructure recovery in Puerto Rico following Hurricane Maria. For this study, the tool was used to extract the change in average nighttime brightness for all census tracts within Puerto Rico and to infer the number of persons without power over time. Multiple opportunities exist to employ CometTS for impactful work in the future including:

• Population dynamics
• Land-use change
• Investigating seasonal or climatic conditions such as drought

Visualizations and analyses derived from CometTS in these topics can inform better understanding of changes to climate, poverty, food security, biodiversity, political conflict, and civil instability.

A visualization of mean observed brightness before and after Hurricane Maria in the San Juan Municipio, Puerto Rico. The linear regression forecast, and seasonal adjusted forecast are plotted in teal and orange respectively. Differences of observed vs. expected brightness can be visualized and quantified.