TITLE: Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions 

ABSTRACT: In the globalized economic world, it has become important to understand the
purpose behind infrastructural and construction initiatives occurring within
developing regions of the earth. This is critical when the financing for such
projects must be coming from external sources, as is occurring throughout major
portions of the African continent. When it comes to imagery analysis to
research these regions, ground and aerial coverage is either non-existent or
not commonly acquired. However, imagery from a large number of commercial,
private, and government satellites have produced enormous datasets with global
coverage, compiling geospatial resources that can be mined and processed using
machine learning algorithms and neural networks. The downside is that a
majority of these geospatial data resources are in a state of technical stasis,
as it is difficult to quickly parse and determine a plan for request and
processing when acquiring satellite image data. A goal of this research is to
allow automated monitoring for largescale infrastructure projects, such as
railways, to determine reliable metrics that define and predict the direction
construction initiatives could take, allowing for a directed monitoring via
narrowed and targeted satellite imagery requests. By utilizing photogrammetric
techniques on available satellite data to create 3D Meshes and Digital Surface
Models (DSM) we hope to effectively predict transport routes. In understanding
the potential directions that largescale transport infrastructure will take
through predictive modeling, it becomes much easier to track, understand, and
monitor progress, especially in areas with limited imagery coverage.
In the globalized economic world, it has become important to understand the
purpose behind infrastructural and construction initiatives occurring within
developing regions of the earth. This is critical when the financing for such
projects must be coming from external sources, as is occurring throughout major
portions of the African continent. When it comes to imagery analysis to
research these regions, ground and aerial coverage is either non-existent or
not commonly acquired. However, imagery from a large number of commercial,
private, and government satellites have produced enormous datasets with global
coverage, compiling geospatial resources that can be mined and processed using
machine learning algorithms and neural networks. The downside is that a
majority of these geospatial data resources are in a state of technical stasis,
as it is difficult to quickly parse and determine a plan for request and
processing when acquiring satellite image data. A goal of this research is to
allow automated monitoring for largescale infrastructure projects, such as
railways, to determine reliable metrics that define and predict the direction
construction initiatives could take, allowing for a directed monitoring via
narrowed and targeted satellite imagery requests. By utilizing photogrammetric
techniques on available satellite data to create 3D Meshes and Digital Surface
Models (DSM) we hope to effectively predict transport routes. In understanding
the potential directions that largescale transport infrastructure will take
through predictive modeling, it becomes much easier to track, understand, and
monitor progress, especially in areas with limited imagery coverage.
Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
http://arxiv.org/abs/2009.00185v1