10 May 2021

Evaluation of Automated Feature Extraction Algorithms Using High-resolution Satellite Imagery Across a Rural-urban Gradient in Two Unique Cities in Developing Countries

Abstract: Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

9 Jul 2020

PUBLICATION NOTICE: Foundations of Mission Analysis Storytelling (FOMAS)

Abstract: Mission analysis is a critical step in military planning and decision-making. It is currently time-consuming for analysts, who have few automated tools. The Foundations of Mission Analysis Storytelling (FOMAS) project developed algorithms, tools, and methods to automate sensemaking for mission analysis, which reduces the time and increases the effectiveness of the process. This report describes the FOMAS research, specifically as it relates to storytelling and link analysis. It includes descriptions of storytelling and a related prototype implementation, “Spatio-temporal Retrieval and Introspection of Data and Embedded Relationships, (STRIDER).” It also describes user engagements involving STRIDER and a prototype information collection and processing tool, the Big Open Source Social Science (BOSSS).