Mobile connectivity has become a fundamental component of modern infrastructure, yet significant spatial inequalities in network access still persist across both urban peripheries and rural environments. Using openly available geospatial datasets, this analysis explores potential mobile coverage gaps by combining OpenCellID cellular infrastructure observations, GHSL population layers and vector data extracted from Overture Maps. The objective is not to reproduce real telecom propagation models, but to generate a simplified spatial estimation of coverage capable of identifying populated areas potentially located outside the influence of nearby cellular infrastructure.
Tag Archives: GHSL
Population Estimation through Dynamic LULC-Based Settlement Validation
The foundational step of this methodology involves the deployment of a centralized processing interface within the Google Earth Engine (GEE) environment. The provided visualization captures the core interface of the custom GEE application, which serves as the hub for the multi-sensor LULC validation pipeline. Within this dashboard, users can define a specific Area of Interest (AOI)—highlighted here over the Iberian Peninsula and North Africa—and configure key parameters, including temporal ranges for the acquisition of sentinel-derived products. Crucially, the interface is designed to load and compare two primary datasets simultaneously: Dynamic World (near real-time, probability-based LULC) and ESA WorldCover (10m resolution structured LULC). The contrasting classification schemes are represented by the legends on the left and right sides of the map view, which illustrate the varying definitions of ‘Built-up’ and urban areas between the two products. Establishing this visual and statistical comparison at the application level is the prerequisite for calculating the spatial disagreement threshold, or delta, that guides the subsequent merging and population estimation phases.
Flood assessment using RADAR (Sentinel1), SRTM v3+ and GHSL pop estimation
I have developed this NRT FLOOD RISK ASSESSMENT monitor in GEE that merges the power of RADAR and GHSL to detect impact over POPULATIONS under any weather conditions.
You can use geospatial intelligence to quickly transform satellite data into critical decisions during climate emergencies. GIS technology driving global resilience!
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