Insights about project management, GIS, and how technology is transforming industries operating in the physical world.
Every URL on this page was tested on 10 September 2026 — status code, content type, response time. 60+ free WMS, WMTS and XYZ endpoints with real layer names, plus the three dead links that still appear on every other list.
How geocoding solves common pain points for energy companies: asset location, network maintenance, solar park planning, and field-to-office integration with accurate maps and AI.
Zip the .shp, .shx, .dbf and .prj, drop the zip on a free browser viewer, and the layer draws with its attribute table. Step by step, then the three things that go wrong — no .prj, accents mangled, two Shapefiles in one zip — each reproduced with GDAL 3.13.3 and the live viewer, outputs quoted.
KML is XML, KMZ is that XML zipped, and <coordinates> are lon,lat in WGS 84 — always. We ran nine experiments with GDAL's KML and LIBKML drivers: the swapped pair that lands in the South Atlantic, the KML driver that drops every attribute on read, styles lost in conversion, and the fixes.
Understand how integrating project management, maps, and artificial intelligence reveals hidden costs in spreadsheets, speeds up decisions, and reduces rework in construction, energy, and utilities.
Losses from soiling, invisible microcracks, inverter failures, and pest damage are real risks that erode the profitability of solar plants. This guide shows how monitoring, mapping, and predictive maintenance cut costs and extend system lifespan.
Tracking environmental data can be chaotic: scattered measurements, manual reports, and the risk of non-compliance. See how to combine field management, georeferenced maps, and automation to simplify monitoring and speed up reporting.
One of our clients in the energy regulatory sector faced manual processes, fragmented data and critical reporting delays. See how we combined field management, GIS and AI to transform inspections, reduce rework and speed up response to nonconformities.
How organizing field work during data collection and inspection of solar panel components reduces costs: planning, use of GIS, digital checklists and AI to avoid rework and optimize maintenance.