A picture is worth a thousand words.
A satellite image is worth a million dollars.
ExoLabs provides tailored software solutions, data products and scientific know-how based on geospatial and remote sensing data to support the private and public sector in gaining insights and efficiency at reduced costs.
Our uniqueness lies in integral and agile data processing from many sources, in particular in the field of near real-time satellite data processing on a global scale.
SILVA Accepted for Funding by Swiss Space Center
SILVA (Satellite-based Inspection of Large Vegetated Areas) was accepted for funding by the Swiss Space Center. Together with the WSL and the ETH EcoVision Lab, ExoLabs will develop EO-based risk-, damage- and value maps for forests.
1st and 2nd Place at Climate Launchpad!
ExoLabs won the national finals of Switzerland and came in second at the Climate Launchpad Regional Final Europe - Sector Resilient Regions!
ExoLabs Receives Funding From PARSEC Accelerator
Together with ThinkOutside and UBIMET we won the PARSEC Accelerator Open Call 2. We are all very much looking forward to provide the most accurate snow monitoring and forecasting system.
Deep Snow Accepted for Innosuisse Funding
Together with the EcoVision Lab at ETH Zurich, the WSL Institute for Snow and Avalanche Research SLF, MountaiNow and Outdooractive, ExoLabs develops an advanced deep learning approach for highly accurate snow depth estimations on a daily basis. This project is realised in the framework of an Innosuisse project.
Snow Data from Space
COSMOS: Daily Maps of Snow Cover, Snow Height and Snow-Water-Equivalent
At ExoLabs we developed and are still actively improving a pipeline from satellite imagery to three main characteristics of snow:
Snow Cover: A global map of probabilities that a respective pixel is covered in snow.
Snow Depth: Regional maps of the height of snow cover.
Snow-Water-Equivalent: Snow can be dry or wet, compact or loose. Snow density combined with snow depth provides the critical information of how much water would be within a cube of snow, if melted.
These models build on top of each other and are unique in their temporal (daily) and spatial (20 m) resolution. The high influence of topography on snow distribution is considered in a novel approach, which full potential is yet to be discovered.
The Power of Earth Observation in the Cloud
AutoMap enables users to download, pre-process, merge and classify thousands of satellite images with just a few clicks.
Originated as tailored solution for an industry partner, AutoMap is the general use-case adaption for the bulk-processing of earth observation satellite data. It enables users to harness the freely available satellite data provided by NASA and ESA, without dealing with clouds, in the sky or the web, process optimisations or storage limitations. Further, it allows users to easily perform their very own, individually optimised, land cover classifications.
Unique Pre-Trained Classifiers
ADELE: Deep Learning for Land Use and Land Cover Changes
Our pre-trained classifiers support the Swiss government to identify 99 different land use / land cover categories within its territory. These classifiers are trained on a unique training set and can be adapted to various use-cases.
The earth is rapidly changing and satellites are the best tool to measure that globally. The quantification of these changes on different scales enable regional, national and international policy-makers to make educated decisions to improve efficiency and sustainability.
As cities become bigger, rural areas fight with an increasing outflow. Urban planning and regional development based on geo-information, statistical models and remote-sensing data help public sector entities to manage and obtain their resources as effectively as possible.
Estimations and Trends
The world's population is affected by changing environmental conditions. Acquiring accurate metrics for strategic management of resources is crucial for an efficient use of ecosystem goods and services and to become more resilient.
Severe weather events put enormous pressure on insurances that manage the risks of farmers, governments or companies. Highly scalable spatio-temporal data is an essential input for modern insurers to model different scenarios for their customers.
Every day, multiple satellites collect petabytes of data from our earth's surface. A lot of those images are freely available, but due to the sheer endless amount of possible applications in science and business services, they are stored in a raw state that satisfies only the most basic needs for as many use-cases as possible. The theme of generic solutions for everybody is symptomatic for the remote sensing community and exactly the reason why ExoLabs exists - We develop solutions that are tailored to your needs!
With us, the complex tasks of pre-processing large amounts of satellite data in different spatial, temporal and spectral dimensions can be dispatched and the prevailing concept of general solutions forgotten. We start building where your idea begins.
ExoLabs is a Swiss company located in Zurich, founded in 2017 as a spin-off of the University of Zurich. Remote sensing is our common interest and scientific background.