Mapflow AI for Radio Network planning –

generating 3D objects from 2D satellite images


Mapflow enables the cost-effective solution to update 3D maps using only 2D imagery automatically. The Mapflow workflow engine is designed for large-scale imagery processing with an average speed of 1 min per 4 Megapixel (~ 1 sq. km VHR imagery). Lidar technology and aerial photogrammetry are the most accurate but much more expensive when it comes to the large coverage. Mapflow AI models leverage segmentation of shadows, regression and other ML techniques to perform on a single satellite image with the claimed accuracy of ±3m - ±4m RMSE (depending on the construction patterns) in a large and dense urban environment. The validation workflow can be enabled to check and increase the results accuracy.

Apply construction detection to identify the recent changes in the urban environment

Construction Detection solution leverages satellite time series to provide reports on the construction & development progress. Optimise your costs for the radio planning by timely mapping of the new buildings around the urban areas

Data streaming

Mapflow enables connection to commercial data streaming services APIs to instantly processing and analyse data without purchasing the images. You can get your data subscription connected to Mapflow platform to optimise traffic costs and analyse the images

GIS and API integration

Our solution can be integrated with GIS (e.g. QGIS-Mapflow plugin provides an immediate workspace for data analysts using free and popular desktop software). Solution developers can start integration using Mapflow public API. The deliverables and API can be customized to meet your workflow requirements and software.

Open geodata – Github

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