Where meets Geoinformatics.
GeoLambda delivers secure AI-powered geospatial analytics solutions that transform location data into climate-resilient insights for enterprise decision-making.
Our Expertise
Leverage our deep research expertise in GeoAI, spatial data science, and AI engineering to transform geodata into climate-resilient solutions with measurable business impact. Our consulting approach helps you identify high-value use cases, design secure AI architectures, and guide successful implementation.
We integrate advanced artificial intelligence with spatial data to solve location-based challenges that traditional GIS cannot address. Drawing on these capabilities, we deliver solutions directly to your organisation — from real-time climate-risk dashboards to AI-secure frameworks and intelligent spatial agents.
Geospatial Analysis
We extract actionable insights from 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, a capability already solving location-based problems.
Machine Learning & AI
Our data-science workbench builds domain-specific AI that is both climate-aware and AI-secure:
- Predictive modelling & pattern recognition for geomorphology, hydrology, biodiversity, emissions forecasting and anthropogenic changes, powered by deep-learning and GeoAI methods.
- Computer vision is used for segmentation, object detection, and reconstruction of photos 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 harmonize 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, 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.
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.
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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.
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Database analytics, mapping and visualization
Implemented ETL pipelines from PostGIS to ArangoDB and built interactive dashboards that cut grid-validation efforts for an energy DSO.
Read moreReady to transform your geospatial data?
We reply within one business day with actionable insights for your location data.