AI for Earth Lab

Use remote sensing and AI to protect the Earth and fight against climate change

partners

Challenges per year

250+

AI for EARTH Engineers

25,000+

hrs of Engineering

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WHAT IS AI FOR EARTH LAB?

Combining the powers of remote sensing, GIS and AI to protect life on Earth

The AI for Earth Lab pools resources and knowledge of our partners, together with the work of our AI for Good Engineers to fight climate change and preserve life on Earth. We use remote sensing and GIS methods and apply machine learning to solve real life problems of our home planet and our life on it. AI for Earth Lab generates a constant stream of AI for Good Challenges for the FruitPunch AI community of data scientists and AI engineers.

The Mission Goals Tech in Focus Challenges Results Participate

WHY AI LAB FOR EARTH PROTECTION?

The need for climate action and protecting the environment has never been more acute

To fight for the preservation of our planet, we need a better and more timely insight into what’s happening on its surface. Satellites, drones and other remote sensing sensors are our eyes in the air detecting, monitoring, and measuring human impact on nature. Thanks to their data we are able to train machine learning models to help detect fires, predict floods, and measure tree growth.

Remote sensing has an incredibly passionate community of researchers, engineers and aficionados rooting for satellites and drones. They see their immense potential to enable geospatial research and turn it into action. For us, for the climate and for all life on earth. Just like them, we are in awe of this technology and its impact. That’s why we are bundling our remote sensing knowledge and resources to address the UN Sustainable Development Goals 15: Life on Land and Goal 13: Climate Action.

Left image: Offshore oil spill off the coast of Peru taken by the SkySat satellite. Courtesy of Planet

Right image: Aerial image of water bunds (a landscape restoration technique) in Tanzania from a drone. Courtesy of Justdiggit

UN Sustainable Development Goal 15: Life on Land

Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss.

Targets

Events

Publications

908

Actions

Find out more about Goal 15

Goal 15 infographic, source: unstats.un.org/sdgs/report/2022/

UN Sustainable Development Goal 13: Climate Action

Take urgent action to combat climate change and its impacts.

Targets

Events

Publications

1502

Actions

Find out more about Goal 13

Goal 13 infographic, source: unstats.un.org/sdgs/report/2022/

WHAT ARE THE GOALS OF THE LAB?

Fight against climate change by applying AI to geospatial data

Predict events dangerous to life on Earth like floods and wildfires

Protect our waters by early detection of threats like pollution

Monitor the success of carbon capture projects

Preserving habitats essential for life of earth

WHAT AI TECH DO WE FOCUS ON?

Remote Sensing

Remote sensing is the process of detecting and monitoring an object or a phenomenon without making physical contact with it. It usually refers to the use of satellite, drone and other sensor technologies to detect and classify objects on Earth. To develop our machine learning models, we use open source and commercial satellite data, drone images and various camera & lidar sensor data collected by our challenge partners.

GIS

Another part of the geospatial field is the geographic information system (GIS). GIS is a system of databases, software tools, procedures and body of knowledge around location relevant data. We apply these technologies in combination with machine learning to understand the geographic context of data and patterns in our Challenges.

Computer Vision

Using satellites & drones to capture what’s going on is useless if we can’t analyze what’s in those images. That’s why in almost every remote sensing challenge we apply a multitude of computer vision techniques that derive meaningful information from images, videos and other visual inputs. We’ve applied it to open source satellite data, commercial hi-res imagery, cam footage from drones and even hydrological sensors distributed along a french river.

Developing high-precision agricultural robots that will increase land yield while reducing resources, with use of digital twins and reinforcement learning.

View challenge View more

THE LAB RESULTS

We use satellite, drone and sensor data to get some machine learning done

Use GIS to map water levels in forests, highlighting flooding risk areas
Create prediction models for floods based on data from hydrological sensors measuring water level, volume and surface speed
Build ML models to estimate propagation speed of flood events
Develop a model to map rivers & its tributaries based on satellite data
Develop a satellite data detection system of human impact sites of -
Illegal deforestation
Wildfires
Coral bleaching
Use GIS and multispectral data to identify and detect oil in satellite and drone imagery, apply U-net architectures to generate masks for the oil spills
Use semi-supervised learning to try to classify the type of leaked oil
Monitor tree coverage and growth from satellite and drone data

SPOTS Robi Beninca launching the flying ranger UAV (unmanned aerial vehicle) & Poacher detection running on the thermal video camera footage on edge hardware of the UAV

Results: Watch and read

From our YouTube channel

From our Publications

February 1, 2024

Tracking Turtles: How AI helps conservationists to re-identify sea turtles

Discover how AI revolutionizes sea turtle conservation, enabling accurate re-identification with non-intrusive methods. This collaboration with Sea Turtle Conservation Bonaire utilizes cutting-edge AI to match sea turtle photos against a database, enhancing our understanding of their migratory patterns and aiding in their preservation. The solution employs innovative techniques for turtle face detection, feature extraction, and matching, proving pivotal for habitat preservation efforts.

November 20, 2023

From Pixels to Preservation: How AI Gives Rise to a Birdwatching Revolution

At the interface of ecology and the ever-increasing applications to use artificial intelligence for good, FruitPunch AI and the Swedish University of Agricultural Sciences (SLU) joined together to better understand how our ecosystems function. Over decades, passionate ornithologists have been taking thousands of images of birds across the world. In the AI for Eagles Challenge, we exploited this dataset to build a machine learning pipeline that would allow rapid assessment of a bird’s behavior, species, and age. Traditional manual labeling of such images is a time-consuming process, and the need for speed in assessing individual birds and population states underscores the urgency of the task. The successful implementation of AI has the potential to revolutionize the efficiency of bird image analysis. By automating the labeling process, the application can significantly accelerate the assessment of individual birds and contribute valuable insights into the broader state of bird populations.

August 25, 2023

Flying High with AI: Counting Pelican Breeding Pairs in the Danube Delta

Imagine flying in a small airplane over the vast wetlands of the Danube Delta on the shores of the Black Sea in Romania looking for patches of small white dots: great white pelicans (Pelecanus onocrotalus). While flying over the colonies researchers like Sebastian Bugariu from the Romanian Ornithological Society (ROS) take photos which will be used to count the number of breeding birds when back in the office. The number of breeding pairs has grown from ~5,000 pairs 15 years ago to recently ~18,000 pairs. Keeping good records of the breeding numbers is important but not an easy task. Back at the office, it can take weeks to go through the images and manually count the pelicans. Wouldn’t it be great if this process could be automated, freeing up time that could be dedicated to other important conservation? This is where the AI for Pelican challenge started.

August 10, 2023

Solving automated wildlife taxonomy with AI

In the wild landscapes of Europe, camera traps have become essential tools for ecologists and wildlife researchers, sharing glimpses into the lives of the continent's diverse and fascinating animals. These devices have revolutionized our understanding of European wildlife, but their use comes with a challenge - the sheer volume of data they generate. To tackle this issue, a team of dedicated FruitPunchers recently developed an AI solution for the European Wildlife Challenge.

July 27, 2023

Listening to the Giants: Protecting Forest Elephants Through Audio Monitoring

In the dense rainforests of Central Africa, a captivating endeavor is underway, driven by researchers from Cornell University. Their mission? To track and protect elephants through the power of audio monitoring. These majestic creatures emit resounding rumbles at an incredibly low frequency, nearly imperceptible to the human ear. These deep calls, traversing vast distances, serve as a concealed communication network for elephants, veiled from our understanding for centuries.

March 23, 2023

Understanding seals with AI

On 6th December 2022, the FruitPunchAI community joined forces with researchers from Colgate University to build a facial recognition model that enables the non-invasive study and monitoring of harbor seals and other marine mammals. The team’s goal was to build upon the existing SealNet model developed by Colgate University to improve its accuracy, generalizability across sea species and mammals, and increase development velocity across data processing and model development workflows.

August 29, 2022

AI-powered Wildlife Conservation in Africa

An account of 10-week teamwork developing multiple machine learning and hardware pipelines to bring production-ready AI to edge hardware on flying rangers.

August 28, 2022

Model Optimization and Pruning of Poacher-detecting YOLOv5

Optimizing a YOLOv5 model for NVIDIA Jetson Nano to increase the inference speed and reduce memory footprint, focusing on inference speed not the absolute mAP.

August 27, 2022

User-friendly, Wilderness-proof MLOps

Developing a CI/CD pipeline that automatically retrains the existing poacher-detecting model, which can be deployed to the drone with improved performance.

August 26, 2022

Autonomous Flight and the Landing of a Fixed-Wing UAV

Developing a model for autonomous landing using a ‘traditional’ technique using GPS beacons a more experimental method of Reinforcement Learning.

Challenge results

Learning Resources

Artificial Intelligence

Time Series Forecasting

FOR INDIVIDUALS

How can you participate?

Become a certified AI for Earth Engineer

Individual engineers who collaborate in the AI for Earth Challenges are trained in working with remote sensing, GIS, computer vision and other skills essential for the applied AI projects.

Sign-up at the platform for free and fill your profile. Pick your interests and join corresponding communities. Create a skill tree you’ll be developing with every AI for Good activity.

Start my journey

Upgrade your skills with AI bootcamps & masterclasses

Learn how to apply your acquired AI knowledge in the real world. Join AI for Good challenges and collaborate in teams all over the globe.

Upgrade my skills

Every activity adds to your development. You’ll be accredited with badges for specific hard and soft skills and certifications after an accomplished challenge.

Build my skill tree

AI for Earth Skills

GIS

Clustering

Timeseries

Remote Sensing

Geospatial Data

Anomaly Detection

U-net

Semantic Segmentation

Data Engineering

Vision Transformers

Deep Learning

ML Pipelines

Hyperspectral data

Object Detection

Supervised Methods

FOR INDIVIDUALS

How can you participate?

Become a certified AI for Wildlife Engineer

Individual engineers who collaborate in the AI for Wildlife Challenges are trained in edge computing, computer vision, model pruning and other skills essential for the applied AI projects.

Join the community

Sign-up at the platform for free and fill your profile. Pick your interests and join corresponding communities. Create a skill tree you’ll be developing with every AI for Good activity.

Start my journey

Start your personalized learning journey

Participate in a challenge

Join our machine learning focused learning events led by experts collaborating with the AI for Good community. Learn with the pros and from one another.

Join the community platform

Get certified

Every activity adds to your development. You’ll be accredited with badges for specific hard and soft skills and certifications after an accomplished challenge.

Get certified

For Organizations

Solve your challenges!

Challenge Partners

Impact organizations and experts applying the results of the AI for Earth Challenges. Do you have a climate change problem that can be solved with AI? Submit your challenge

Contribute!

Contributing Partners

Use their teams, expertise and resources to advance AI for Earth Lab & Challenges. Can your experts or technology contribute to solving an AI for Earth Challenge? Partner up! 👇

Get your community involved!

Community Partners

Use their reach to spread the AI for Good news, advocate for AI for Good Lab & Challenges. Do you have a network we could reach out to with environmental or climate action AI topics? Connect on Linkedin, Twitter or drop us a note 👇