UP42 announced that the NEXTMap Elevation Data Suite from Intermap Technologies is now offered on the UP42 developer platform for Earth observation data and analytics.
The NEXTMap 3D elevation products are available as Digital Surface Models (DSM) and Digital Terrain Models (DTM) at one-, five-, and 10-meter resolution. The addition of NEXTMap datasets to the UP42 marketplace enables users to build even more powerful geospatial solutions in the areas of infrastructure management, construction planning, geologic mapping, land cover classification, forestry, resource conservation, and contour generation.
UP42 gives users direct access to extensive Earth observation datasets and advanced processing algorithms – along with cloud computing power – to create their own geospatial solutions easily and inexpensively. Users purchase just the data needed to cover their area of interest and then leverage scalable processing capabilities to analyze the datasets without investment in their own computing infrastructure.
Based in Englewood, Colorado, Intermap Technologies is a provider of geospatial datasets, solutions, and software. The NEXTMap 3D products are seamless digital elevation models derived from satellite imagery and are available worldwide. The NEXTMap DSMs deliver rich feature content, including vegetation, building structures and roads. The DTMs are bare-earth elevation models from which surface features have been removed.
Three NEXTMap digital elevation datasets are now available from UP42:
- NEXTMap One with 1m spatial resolution, 1m vertical and 3.5m horizontal accuracy.
- NEXTMap 5 with 5m resolution and 1.6m vertical and 3.5m horizontal accuracy.
- NEXTMap 10 with 10m resolution and 8.4m vertical and 17.5m horizontal accuracy.
The NEXTMap datasets join a variety of Earth observation information already on the UP42 marketplace, including Pleiades 1A/B, SPOT 6/7, Landsat-8, TerraSar-X, Sentinel-2 and MODIS satellite imagery, Getmapping U.K. aerial data, exactEarth AIS data, and Meteomatics weather and ocean data. Leveraging these datasets, users may apply more than 50 geospatial analytics processes, including machine learning algorithms, to automatically find features, count objects, detect change, uncover patterns, classify land use, and derive vegetative indices.