English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In the globalized economic world, it has become important to understand thepurpose behind infrastructural and construction initiatives occurring withindeveloping regions of the earth. This is critical when the financing for suchprojects must be coming from external sources, as is occurring throughout majorportions of the African continent. When it comes to imagery analysis toresearch these regions, ground and aerial coverage is either non-existent ornot commonly acquired. However, imagery from a large number of commercial,private, and government satellites have produced enormous datasets with globalcoverage, compiling geospatial resources that can be mined and processed usingmachine learning algorithms and neural networks. The downside is that amajority 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 andprocessing when acquiring satellite image data. A goal of this research is toallow automated monitoring for largescale infrastructure projects, such asrailways, to determine reliable metrics that define and predict the directionconstruction initiatives could take, allowing for a directed monitoring vianarrowed and targeted satellite imagery requests. By utilizing photogrammetrictechniques on available satellite data to create 3D Meshes and Digital SurfaceModels (DSM) we hope to effectively predict transport routes. In understandingthe potential directions that largescale transport infrastructure will takethrough predictive modeling, it becomes much easier to track, understand, andmonitor progress, especially in areas with limited imagery coverage.

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