Where AI meets Geoinformatics.
We build AI tools and pipelines for geospatial data — grounded in earth science, production-ready. Since 2018.
Our Expertise
Our research expertise in GeoAI, data science and AI engineering underpins solutions that go beyond conventional GIS. We solve location-based challenges where traditional GIS workflows fall short — from climate risk analysis to secure AI frameworks and intelligent spatial agents.
We have been developing machine learning and deep learning methods for geospatial data since 2018. Among other projects, we built an ML application for automated scientific discovery for a Max Planck and a Helmholtz institute, now available as open source. For GEO DATA GmbH we took an end-to-end pipeline for construction-trench segmentation into production on Azure, aligned with the EU AI Act.
It starts with the right use case and the right architecture. Our AI Architecture Assessment turns this into a defined deliverable. We also share what we know in compact training courses and workshops — from executive briefings to hands-on sessions for technical teams.
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.