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.

1 Sep 2020

PUBLICATION NOTICE: Isarithmic mapping of radio-frequency noise in the urban environment

Abstract: Radio-frequency (RF) background noise is a spatially-varying and critical parameter for predicting radio communication system and electromagnetic sensor performance in urban environments. Previous studies have measured urban RF noise at fixed, representative locations. The Cold Regions Research and Engineering Laboratory (CRREL) has developed a tunable system for conducting mobile RF noise measurements in the VHF and UHF and shown that urban RF noise characteristics vary significantly and repeatably at a scale of tens of meters (Haedrich & Breton, 2019). CRREL also found high-powered regions in Boston, MA that are persistent over time. However, since previous studies conducted stationary measurements or measurements along linear transects, little is known about the 2-dimensional topography of urban noise and the spatial distribution and characteristics of these high-powered regions. In this paper, we present the results of a dense, block-grid survey of downtown Boston, MA at 142 and 246.5 MHz with measurements taken every meter along each street. We present isarithmic maps of median noise power and describe the spatial distribution, shape and other characteristics of the high-powered regions. We compare the rate of noise power decay around high-powered regions to losses predicted by a power law model of path loss.

11 Aug 2020

PUBLICATION NOTICE: The Urban Ground-to-Ground Radio-Frequency Channel: Measurement and Modeling in the Ultrahigh Frequency Band

ABSTRACT:  Ground-to-ground radio communication and sensing within the urban environment is challenging because line of sight between transmitter and receiver is rarely available. Therefore, radio links are often critically reliant on reflection and scattering from built structures. Little is known about the scattering strength of different buildings or whether such differences are important to the urban ground-to-ground channel. We tested the hypotheses that (1) diffuse scattering from built structures significantly impacts the urban channel and (2) scattering strength of urban structures varies with surface roughness and materials.  We tested these hypotheses by measuring urban channels in Concord, New Hampshire, and Boston, Massachusetts, and via channel-modeling efforts with three-dimensional representations of the urban environment. Direct comparison between measured and modeled channels suggest that both of these hypotheses are true. Further, it appears that ray-tracing approaches underestimate the complexity of urban channels because these approaches lack the physical processes to correctly assess the power incident on and scattered from built structures. We developed a radio-geospatial model that better accounts for incident power on both directly visible and occluded buildings and show that our model predictions com-pare more favorably with measured channels than those channels predicted via typical ray-tracing approaches.

23 Apr 2020

PUBLICATION NOTICE: Optimized Low Size, Weight, Power and Cost (SWaP-C) Payload for Mapping Interiors and Subterranean on an Unmanned Ground Vehicle

ABSTRACT: Section 3 of the FY15 Force 2025 Maneuvers Annual Report indicates that in Dense Urban Areas (DUA), specifically in a subsurface, surface, or super-surface structure, the ability to identify threats will be diminished. Most commercially available LIght Detection And Ranging (LIDAR) systems are specifically designed for high-resolution aerial imaging and mapping applications. As a result, they tend to be large, heavy, power-hungry, data bandwidth intensive, and expensive. They also employ lasers that are not typically eye-safe, which limits their overall effectiveness in subterranean and the interiors of subsurface or super-surface structures. However, due to recent advances in the automotive industry, there are new generations of Size, Weight, Power, and Cost (SWaP-C) sensors that are eye-safe, making them suitable for use indoors and in subterranean environments. While these tradeoffs limit their effective use to hundreds of meters (compared to kilometers for their more expensive counterparts), they are ideal candidates for use in subterranean and building interiors. While cameras fill this niche to some extent, the volumetric calculations provided by these sensors provide additional intelligence to shape the security of the environment and offer more precision when maneuvering troops. These sensors would provide the warfighter with situational understanding in previously inaccessible locations. Therefore, to aid in the Army’s need to obtain and maintain situational understanding in DUAs, the authors propose utilizing low size, weight, power, and cost (SWaP-C) sensors, on a robot platform, for surveying and mapping underground structures and building interiors. Rapid/near real-time data processing is possible by utilizing open-source software and commercial off the shelf (COTS) components. Using the preferred sensor payload autonomously was also explored.