The Center is an interdisciplinary research platform for:

• creating and implementing innovative technological solutions integrating artificial intelligence (AI), satellite data and geographic information systems (GIS) in urban ecology and agricultural lands
• developing and implementing new environmentally friendly crop cultivation technologies to ensure food security and sustainable development

Center's Resources

The laboratory facilities enable a comprehensive approach to ecosystem analysis and management as it combines advanced technologies and interdisciplinary methods to address critical challenges:  

• transforming raw data (satellite imagery, DEMs, soil samples) into strategic decisions—from risk mapping to adaptive agricultural technologies—ensuring a balance between infrastructure development and environmental sustainability;
• using spatial modeling and forecasting methods to assess landscape geostructure state, urban development projects, land plots and water bodies;
• modeling the development impact on microclimate and water balance using GIS and neural networks (e.g., assessing heat islands and drainage systems);
• predicting flooding and inundation risks using hydrological models that incorporate DEM data and historical precipitation;
• assessing environmental safety and the risks of anthropogenic impacts on soil and water bodies;
• providing environmental assessments of soil structures and agricultural cultivation practices;
• using DEMs (digital elevation models) for 3D visualization of terrain, calculation of slopes, flood zones, and runoff;
• predicting soil erosion by integrating RUSLE with machine learning (which allows for considering not only static parameters (e.g., slope steepness) but also dynamic factors (land cover changes, climate anomalies);
• conducting groundwater analysis: ML models based on super-resolution satellite imagery (0.5 m) reveal the relationship between urbanization, water levels and water quality;
• assessing soil degradation using ML algorithms that process satellite data (NDVI, humidity) and field sensors;
• implementing precision farming using autonomous equipment that adapts fertilizer application and irrigation based on ML yield forecasts and DEM data;
• developing adaptive agricultural technologies, including crop selection based on projected changes in groundwater levels and climate (based on GWQI).

Equipment Fleet

The Project Lab has:

Modern software and computing systems based on computer stations:

• SCAD, LIRA — for engineering modeling of geostructure and infrastructure stability (e.g., analyzing the load on slopes subject to erosion);
• Ekolog Unified Program for Calculating Atmospheric Pollution — to assess air, water and soil pollution in urban areas, including predicting the spread of pollutants;
• ArcGIS Pro, QGIS — for spatial analysis of DEMs, integrating RUSLE models and creating environmental risk maps;
• ENVI, ERDAS Imagine — processing hyperspectral and multispectral satellite data to monitor soil degradation and water quality;
• TensorFlow, PyTorch — ML frameworks for training neural networks (e.g., CNN for image super-resolution or GWQI forecasting);
• HEC-RAS, SWMM — hydrological modeling for flood forecasting and optimization of urban drainage systems.

Computer-aided design (CAD) systems:

• AutoCAD and ZwCAD — for capital construction projects and erosion control systems using DEM and RUSLE data;
• REVIT and Civil 3D — for 3D modeling of smart urban infrastructure, including green space and utility systems;
• Bentley Systems (OpenGround and ContextCapture) — for geotechnical risk analysis and landscape digital twin creation;
• Global Mapper — to generate high-precision DEMs based on UAV and LiDAR data to calculate LS factors in RUSLE.

DJI FPV Combo FD1W4K unmanned aerial vehicle (UAV) with camera and software:

• Pix4D and Agisoft Metashape — for photogrammetric UAV data processing to create orthophotomaps and 3D landscape models;
• DroneDeploy — for mapping agricultural land, monitoring crop conditions and identifying soil salinization zones;
• eMotion (Parrot) — planning autonomous UAV flights for surveying hard-to-reach areas;
• RStudio, Python (GDAL, Scikit-learn libraries) — UAV data analysis in conjunction with ML algorithms for predicting land degradation dynamics.

Example of technology integration at the Research Center: UAV data is processed in Agisoft Metashape to create a DEM, which is imported into ArcGIS and RUSLE to calculate soil loss. Machine learning models on TensorFlow then refine the forecasts, and the results are visualized in Civil 3D to design protective measures. This allows the Center to combine field data, satellite imagery and modeling into a unified ecosystem for sustainable resource management.

The green (soil-ecological) laboratory has:

• SF-56 Spectrophotometer
• BEING Incubator
• Levenhuk MED 5M LCD Screen Microscopes
• DHG-9035A Drying Oven
• BI-120TL Heating Incubator
• U1-MOK-1MT Gluten Washer
• CM-70M-07 Centrifuges
• MOK-3M and IDK-3M Gluten Testing Equipment
• Oleo-Mac MB 90 Mistblower
• Protein-1M Grain Analyzer
• N-Tester Nitrogen Analyzer  
• Soil Parameter Sensors (NDVI, moisture, pH, temperature)

Research is conducted both in the laboratory and in experimental fields in the Moscow and Tver regions.

The Center's team is international, and it includes researchers from leading scientific organizations and universities of Russia, China, Egypt, Algeria, Tunisia, Austria and the United States collaborating with RUDN University Institute of Ecology researchers. In addition to renowned scientists, including a member of the Russian Academy of Sciences and a corresponding member of the Russian Ecological Academy, the Center also employs young researchers, PhDs, junior research fellows, research assistants, postgraduate students and students, thereby fostering a scientific school.

Since its opening in 2020 following a RUDN University competition to establish scientific laboratories, the Center has been fully self-sufficient, it has implemented over 40 research and development projects totaling over 50 million rubles through contractual arrangements, grant support, and the commercialization of the resulting intellectual property. The team formed as a result of the Center's creation demonstrates the importance and effectiveness of RUDN University's investment in establishing such laboratories.

Partners

• National Higher Agronomy School (Algiers)
• Assiut University (Egypt)
• Faculty of Agriculture, Kafr el-Sheikh University (Egypt)
• Tanta University (Egypt)
• Agricultural University of Havana (Cuba)
• National Authority for Remote Sensing and Space Sciences (Egypt)
• French National Institute for Agriculture, Food and the Environment (France)
• Regional Center for Agricultural Research. Sidi Bouzid (Tunisia)
• Chengdu University of Technology, Sichuan Province (China)
• Laboratory of Information Technology for Geodesy, Cartography and Remote Sensing, Wuhan University, Wuhan (China)
• Department of Land Resources and Environment, Hamelmalo Agricultural College, Keren, (Eritrea)
• GLA University (India)
• Mississippi State University, Starkville (USA)
• International Institute for Applied Systems Analysis, Laxenburg (Austria)
• Geospatial Analysis Center, University of Miami, Oxford (USA)
• Ryazan State Agrarian and Technological University
• Russian Society of Civil Engineers
• Dokuchaev Soil Institute
• VidProekt Research and Design Institute
• AgroEco Research and Production Company