Geospatial Data Scientist | Scientific Software Engineer

Sun, 10 May 2026 00:00:00 +0000

Developing AI-Powered Environmental Intelligence Systems

At Brockmann Geomatics, I work as a Data Scientist, developing production-grade Python workflows and machine learning solutions that transform large and heterogeneous Earth observation datasets into reliable analytical products. My work combines scientific software engineering, geospatial analytics, and applied artificial intelligence to support environmental monitoring, water analytics, and climate-related decision-making.

I currently contribute to international initiatives including AQUATIME, a European Space Agency Sentinel User Preparation project advancing the use of future Copernicus CHIME and LSTM missions for aquatic ecosystem and biodiversity monitoring and OASIS, a Horizon Europe project focused on AI-powered Earth Intelligence for water resilience and ecosystem management.

The role sits at the intersection of Earth observation, scientific computing, and scalable software development, with strong emphasis on reproducibility, validation, and maintainable code.

Core Responsibilities

My responsibilities include designing and maintaining end-to-end workflows that ingest, process, validate, and analyze multi-source geospatial and Earth observation datasets.

My Contributions

Python-Based Data Pipelines: Develop modular and reproducible workflows for ingesting, harmonizing, and processing large environmental and infrastructure datasets.
Machine Learning & Time-Series Analysis: Apply statistical and machine learning methods to extract patterns, trends, and predictive insights from satellite and environmental data.
Data Quality & Validation: Implement automated checks, logging, and monitoring to ensure reliability, traceability, and reproducibility.
Scientific Software Engineering: Contribute to maintainable codebases using version control, experiment tracking, and structured documentation.
Stakeholder Collaboration: Translate scientific and operational requirements into scalable analytical solutions and decision-support outputs.

Key Projects

AQUATIME

An European Space Agency (ESA) Sentinel User Preparation project focused on preparing the synergistic use of upcoming Copernicus CHIME and LSTM missions to improve understanding of aquatic ecosystems and biodiversity.

The project develops novel algorithms and products for phytoplankton monitoring, harmful algal bloom detection, and ecosystem analysis across multiple study areas in Northern Europe and the North Sea. Applications include aquaculture risk assessment, water quality monitoring, biodiversity analysis, and climate-driven ecosystem dynamics.

The consortium is led by Brockmann Geomatics and includes partners such as the Finnish Environment Institute (SYKE), Spectral Earth, Brockmann Consult, the Royal Belgian Institute of Natural Sciences, the University of Tartu, and the Swedish University of Agricultural Sciences.

OASIS (Observation and AI-powered System for Intelligent Water Solutions)

OASIS is a four-year Horizon Europe project (Grant Agreement 110293392) that delivers AI-powered Earth Intelligence to improve water quality monitoring, ecosystem management, biodiversity assessment, and climate resilience. The project combines Earth Observation, artificial intelligence, environmental models, and in situ measurements to support evidence-based policymaking and the implementation of key European directives such as the Water Framework Directive, Marine Strategy Framework Directive, Natura 2000, and the Nature Restoration Regulation.

The consortium brings together 16 leading organizations and 15 case-study areas across Europe, Africa, and South America, creating a globally connected platform for water resilience and sustainable ecosystem management. The project contributes directly to the European Green Deal and the United Nations Sustainable Development Goals, particularly SDG 6 (Clean Water and Sanitation) and SDG 14 (Life Below Water).

At Brockmann Geomatics, I contribute as a Data Scientist to the development of scalable Python workflows, machine learning methods, and geospatial analytics pipelines that transform multi-source Earth observation and environmental datasets into reproducible, decision-ready intelligence products.

The consortium is coordinated by the Finnish Environment Institute (SYKE) and includes internationally recognized partners such as Deltares, VITO, Finnish Meteorological Institute, Brockmann Consult, University of Tartu, Leipzig University, and several other research and industry organizations.

Technical Stack

Programming & Analytics: Python, SQL, Jupyter
Scientific Libraries: pandas, NumPy, xarray, MLflow
Geospatial Libraries: GeoPandas, GDAL, Rasterio
Development Tools: Git, Linux
Methods: Machine Learning, Time-Series Analysis, Data Validation

Professional Growth and Impact

This role is strengthening my capabilities in:

Data Science and Applied AI
Scientific Software Engineering
Environmental and Climate Analytics
Geospatial Data Engineering
Reproducible Research and Experiment Tracking

Working at Brockmann Geomatics allows me to combine deep technical expertise with meaningful environmental applications, building scalable systems that transform complex Earth observation data into actionable intelligence.

Additional Resources:

Official Website: https://www.brockmann-geomatics.se/

LinkedIn: https://www.linkedin.com/company/brockmann-geomatics/

GitHub: https://github.com/bcdev/

AQUATIME Project: https://sup.apex.esa.int/en/projects/

OASIS Project: https://oasis-he.eu/

Core Competencies

Tue, 01 Jul 2025 00:00:00 +0000

Remote sensing fuels modern geospatial intelligence—bridging environmental monitoring, disaster response, and spatial decision-making through advanced Earth Observation analytics

Geospatial Intelligence	Remote Sensing Applications	Raster Workflow Engineering	Statistical and ML Methods	Multiscale Analysis
Satellite SAR data analytics (X, C, L bands) Multispectral and hyperspectral imagery analysis DEM generation and terrain surface modeling Land use/land cover dynamics and transitions Topographic correction and geomorphological analysis	Post-disaster impact mapping and response support Urban climate modeling and heat island detection SAR-based snow and glacier characterization Deforestation and forest health monitoring Urbanization trends and land consumption analysis	Multi-sensor preprocessing (RTC, speckle filtering) Time-series change detection automation Supervised/unsupervised raster classification Hexgrid and tile-based pipeline design Real-time raster-to-vector product conversion	Spatiotemporal trend analysis using SAR data Multivariate regression & clustering models Outlier & anomaly detection for disaster insights Dimensionality reduction for raster analytics Impact score modeling for spatial risk profiling	Building-level exposure and footprint analysis Parcel-to-region aggregation using spatial grids Dynamic downscaling for urban/rural transitions Resolution-aware modeling across nested scales Cross-scale spatial normalization and fusion

Climate Analytics | Udit Asopa