Fast dynamics of urban areas is enforced by investment decisions. Development markets can be surveyed using instance segmentation and Change Detection methods - to estimate and classify buildings and assess population. We apply photogrametry and indirect methods to reconstruct buildings heights and distinguish one type from another.
Deforestation and tree cover are the common targets for remote sensing. Our applied works include early detection and monitoring of wildfires, classification of tree losses. We cooperate with IoT Lab at Skoltech to propose new combined measurments from the set of various sensors. Our partnership with Planet Inc. allows to create and implement the unique data models for the monitoring of the forestry.
We use algorithms based on deep learning models for classification of crops and detection of anomalies in vegetation growth. Collaborating with industrial partners and data providers we implement ML into real business processes of the agriculture workflows.
We are involved in actively research of change detection and object recognition in satellite and aerial imagery according to risk mitigation and filed work optimization for pipeline operator companies.
The use of various sources of commercial satellite data allows us to fuse data to achieve the maximum coverage at optimal rate. In the current research we are focusing on the overgrowing of the powerline glades for clearing tasks and control.
Geoalert company is providing the geoinformation platform that powers automatic analysis of Earth Observation data by pretrained Machine learning algorithms. Using Geoalert platform customers can setup tasks for object recognition and define a territory of interest to get analytics on recent Earth Observation data.
See how it works in the applications:
We use data provided by the leading global satellite operators as well as data acquired by local aerial imagery companies.
See our page on github