Innovation & Research

Advanced Analytics from Remote Sensing Data

Explanation of the Process

WEO use cutting-edge algorithms and data pipelines  to extract valuable, high-resolution insights from regularly updated global satellite datasets.

We implement our analytics as operational services, not once-off studies. We prioritise ongoing and long-term solutions, ensuring clients have access to regular updates for their risk management/vegetation/planning needs.

First, satellite imagery is harvested with a global and regular coverage from visible, thermal and radar sensors.

Next, raw imagery is processed to improve data consistency by reducing the impact of clouds, shadows and calibration differences. Advanced machine learning then extracts high resolution analytics from lower resolution data.

Thirdly, verified data outputs are delivered directly into internal systems for decision-making.

Research Projects

We are a multi-disciplinary team of remote sensing, engineering, machine learning, project management and development experts with a shared background in understanding the environment. 

Within 3 years of its founding, WEO has led five European funded feasibility studies, pilot  and research projects. Beyond this we are also specialists in developing customised, state-of-the-art solutions for customers who wish to push the boundaries. We know that applied research shouldn’t take years and  focus on delivering practical results to clients within pre-agreed timeframes.

We are a research partner who can be trusted to deliver as prime or subcontractor, from large Horizon Europe projects to small investigations with tight timelines.

Tree Monitoring

Boosting urban forestry with tree health and growth monitoring, as well as supporting industry to manage the risk of trees impacting critical infrastructure in this project supported by ESA and LSA.

Urban Green and Liveability Tracker

Building next-generation analytics to support urban developers and cities to create more liveable and sustainable neighbourhoods. Supported by the European (ESA) and Luxembourg Space Agencies (LSA).

Water Management

The Sustainable WAteR Management from earth observation (SWARM) project aims to improve management of urban and rural water cycles by creating new tools with satellite data. Supported by ESA and LSA.


This Marie-Curie Fellowship project bolsters urban flood resilience by using remote sensing, crowdsourcing, and deep learning to improve flood mapping.


WEO began a partnership with Kolpa Nature Park in Slovenia to monitor the health of protected centuries-old trees within the park and the impact of human activities.


Can we trust remote sensing evapotranspiration products over Africa?

Weerasinghe et al. 2020

Using Remote Sensing Based Metrics to Quantify the Hydrological Response in a City

Wirion et al. 2019

The importance of city trees for reducing net rainfall: comparing measurements and simulations

Smets, Wirion et al. 2019

Location- and Time-Specific Hydrological Simulations with Multi-Resolution Remote Sensing Data in Urban Areas

Wirion et al. 2017

Transfer Learning with Convolutional Neural Networks for Rainfall Detection in Single Images

Notarangelo et al. 2021

Opportunistic Rainfall Monitoring from Single Pictures Using Artificial Intelligence

Notarangelo et al. 2022

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