Where AI meets Geoinformatics.
We build AI tools and pipelines for geospatial data — grounded in earth science, production-ready.
Our Expertise
Our research expertise in GeoAI, spatial data science and AI engineering enables us to build solutions that go beyond conventional GIS approaches — identifying the right application, designing secure AI architectures and seeing projects through to production.
We combine advanced AI with spatial data to solve location-based challenges that traditional GIS cannot address — from real-time climate-risk dashboards to secure AI frameworks and intelligent spatial agents.
Geospatial Analysis
We analyse location data by combining the following:
- Advanced GIS & spatial statistics for network analysis, hotspot detection and exposure scoring for critical assets and infrastructure.
- High-resolution satellite & aerial imagery for automated change detection and environmental-impact monitoring with state-of-the-art computer-vision pipelines.
- Custom web-mapping solutions for interactive dashboards that keep domain experts one click away from decisive evidence.
These workflows shorten the path from raw pixels to climate-risk scores.
Machine Learning & AI
Our data-science workbench builds domain-specific AI for geospatial challenges, with security built in:
- Predictive modelling & pattern recognition for geomorphology, hydrology, biodiversity, emissions forecasting and anthropogenic changes, powered by deep-learning and GeoAI methods.
- Computer vision for segmentation, object detection, and reconstruction of photographic and multispectral imagery.
- Geo-NLP & spatial AI agents that let users query spatial data in natural language and orchestrate multi-agent decision workflows.
Every model passes our Secure-AI checklist — covering explainability, bias audits and adversarial hardening — developed in line with the European Commission's JRC guidance on harmonised AI-Act standards, the security specifications of ETSI TC-SAI and the independent safety recommendations of the Future of Life Institute.
Data Engineering
Robust data pipelines are the backbone of any GeoAI solution. We design cloud-native architectures that:
- Ingest, clean, and harmonise terabyte-scale geospatial data from sensors, satellites, and open-data portals.
- Scale elastically on Kubernetes clusters and cloud stacks such as Azure, AWS and private infrastructure, ensuring cost-efficient burst capacity.
- Serve analytics in near real-time via secure APIs that feed dashboards, digital twins, and climate-risk platforms.
All pipelines include lineage tracking and granular security controls so future developers can extend the system without compromising trust or compliance.
Project Highlights
Selected projects delivered by our founder — now continued through GeoLambda GmbH.
Deep learning methods (AI) for geodata analysis
Developed and trained neural networks using PyTorch for segmenting construction trenches, with end-to-end pipelines in Azure Machine Learning following EU AI Act requirements.
Read more
MLOps, AutoML and Web Development
Built a geospatial test dataset in Python, compared leading AutoML/MLOps stacks, and delivered a roadmap for a GIS software vendor's research program.
Read more
Data Wrangling and ML-App Development
For Max-Planck and Helmholtz Institutes, created a web-scraped geodata corpus and an open-source Streamlit app that lets scientists run automated scientific discoveries.
Read moreReady to transform your geospatial data?
We reply within one business day.