About

The laboratory was organized at Skoltech Center for Computational and Data-Intensive Science and Engineering (CDISE) to develop and apply Machine learning algorithms for intelligent analysis of Earth observation data. It was supported by the National Technological Initiative and is partnering with the University of Innopolis.
We cooperate with Skoltech Space Center and other research groups and we are open to collaboration in the area of applied Machine learning, remote sensning data processing, designing EO downstream applications.

If you're interested in Machine Learning, Remote sensing and cloud services - please, pay attention to all our openings.

Industries

Building & Construction

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.

Forest monitoring

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.

Agriculture

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.

Monitoring of pipeline infrastructure

We are involved in actively research of change detection and object recognition in satellite and aerial imagery according to risk mitigation and costs optimisation for pipeline operator companies.

Monitoring of powerlines

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.

Research

Open spatial dataset

“Open Spatial Dataset” is the joint project to provide RnD community with open data and benchmarks for ML analysis of Remote Sensing data.
The labeling data is specified according to wide classification range of the natural and man-made objects that have a clear interpretation either in satellite or aerial imagery.

Geoalert

The geoinformation platform that powers automatic analysis of Earth Observation data provided by World leading operators. One can setup tasks for object recognition and define a territory of interest to get analytic reports and notifications of changes based on the recent satellite imagery.

See how it works in the applications:

News

GISTech conference in Innopolis

In December 14 AeronetLab of CDISE, Skoltech, took part in the GIS Tech Russia conference, organized by university of Innopolis. Georgy Potapov presented on the topic “How to calculate people from Space”.
It was shown how to collect buildings and population data in a feasible and time-efficient way using large amounts of satellite imagery and processing algorithms powered by neural networks. Such applications are useful for location intelligence and business planners since the companies in this markets usualy have either limitted financial or time resources to carry out detailed field reports. The method is based on the current research in progress within “Geoalert” platform, which enables automatic analysis of Earth Observation data provided by World leading satellite operators.
5.0
New model for Forest detection and monitoring
Within the project of "Digital Model of Tatarstan" the new model for forest segmentation was created. The model will be used to detect such phenomena as overgrown of glades for protected areas of powerlines. It's adjustable for different resolution and was tested particular on SPOT imagery (1.5m) - to be applied to the whole territory of Tatarstan.
4.0
Vacancy - Research Scientist
Skoltech - AeroNet Lab is looking for a Research Scientist to join our team in new fascinating projects of 3D-data analysis and development and impelmentation of algorithms for remote sensing imagery processing.
(If you're interested in Machine Learning, Remote sensing and cloud services - please, pay attention to all our openings).
3.0
Our team won at Machine Learning Hackathon "Map Augury" / May 21, 2018
Our team won the 2nd prize at hackathon of the ML applications for remote sensing data in the University of Innopolis. We presented our new project on GeoAlert platform - CityEye.
2.0
Open dataset service / May 19, 2018
We started publishing first datasets of the "Open dataset" service - the catalog of remote sensing labeled imagery for machine learning applications.
The one based on the Digitalglobe satellite imagery is aimed for Emergency mapping applications. It contains the baseline Model of instance segmentation and detection of the damaged houses in the residential areas of California region.
For dataset please visit our github page.
1.0