Competitions

Help conservationists make use of satellite imagery to estimate the aboveground biomass of Finnish forests.

Help NASA scientists identify the chemical composition of rock and soil samples for Mars planetary science.

Help the Bureau of Ocean Energy Management (BOEM), NOAA Fisheries, and Wild Me accurately identify endangered Cook Inlet beluga whales from photographic imagery. Scalable photo-identification of individuals is critical to population assessment, management, and protection for these endangered whales.

Help NASA scientists identify the chemical composition of rock and soil samples for Mars planetary science.

Clouds obscure important ground-level features in satellite images, complicating their use in downstream applications. Build algorithms for cloud cover detection using a new cloud dataset and Microsoft's Planetary Computer!

Help conservationists monitor wildlife populations. In this challenge, you will estimate the depth of animals in camera trap videos. Better distance estimation models can rapidly accelerate the availability of critical information for wildlife monitoring and conservation.

Help Microsoft AI for Earth and Cloud to Street detect floodwater through cloud coverage using Sentinel-1 synthetic-aperture radar (SAR) imagery. Accurate flood mapping can save lives by strengthening early warning systems and directing emergency relief.

Help make overhead imagery more useful for time-sensitive applications like disaster response. Build computer vision algorithms that can effectively model the height and pose of ground objects for monocular satellite images taken from oblique angles.

Help NOAA better forecast changes in Earth’s magnetic field! Improved models can provide more advanced warning of geomagnetic storms and reduce errors in magnetic navigation systems.

Throughout a tropical cyclone, humanitarian response efforts hinge on accurate storm intensity estimates. Using satellite images assembled by Radiant Earth Foundation and the NASA IMPACT team, can you estimate the wind speeds of storms at different times?

The objective of this challenge is to detect the most severe epithelial lesions of the uterine cervix. The challenge brings together thousands of microscopic slides from different medical centers across France.

Genetic engineering is a powerful tool that demands responsible use. Your goal is to create an algorithm that identifies the lab-of-origin for genetically engineered DNA with the highest accuracy level possible.

Help accelerate Alzheimer's research by automatically classifying which blood vessels are flowing and which are stalled.

Can you map building footprints from drone imagery? This semantic segmentation challenge leverages computer vision and data from OpenStreetMap to support disaster risk management efforts in cities across Africa.

Can you predict which animals are present in camera trap images? Leverage millions of images of animals on the Serengeti to build a classifier that distinguishes between gazelles, lions, and more!

Can you predict the roof material of buildings from drone imagery? Leverage aerial imagery in St. Lucia, Guatemala, and Colombia to more accurately map disaster risk at scale.

Help make industrial cleaning processes more efficient! The goal of this competition is to predict measures of cleanliness during final rinse in order to help minimize the use of water, energy and time, all while ensuring high cleaning standards.

Usually with buildings, bigger historic datasets yield more accurate consumption forecasts. The goal of this challenge is to provide an accurate forecast from the very beginning of the building instrumentation life, without much consumption history.

Storage is critical to flexible and reliable access to renewable energy sources. In this challenge, competitors combined traditional optimization methods and machine learning to build algorithms for controlling a battery charging system as efficiently as possible.

Commercial buildings waste an estimated 15% to 30% of energy used due to poorly maintained, degraded, and improperly controlled equipment. Competitors built quick-response algorithms to find anomalies in energy use and elevate them for human attention and intervention.

More accurate forecasts of building energy consumption mean better planning and more efficient energy use. In this challenge, competitors used machine learning to build the most accurate predictions of the future from limited data in the past.

Measuring poverty is hard. Thanks to the efforts of thousands of competitors, The World Bank can now build on open source machine learning tools to help predict poverty, optimize uses of survey data, and support work to end extreme poverty …

Data scientists from more than 90 countries around the world drew on 300,000 video clips in a competition to build the best machine learning models for identifying wildlife from camera trap footage. The results are powerful and – equally important …

Sustainable fishing means tracking every fish caught. New tools using automated video processing and artificial intelligence can help responsible fisheries comply with regulations, save time, and lower the safety risk and cost from an auditor on board.

Competitors built hundreds of algorithms to predict changes in Antarctic penguin populations from the most comprehensive counts available. These algorithms give researchers a greater understanding of penguin population dynamics, a leading indicator of climate change.

US presidential elections come but once every 4 years, and this one's a big one. There are lots of people trying to predict what will happen. Can you top them?

Contribute to open, cutting-edge research on the use of wearables in promoting health and independence for seniors.

Model the water output from water-collecting fog nets in Southwest Morocco. Accurate predictions can improve collection efforts and enable greater access to fresh water throughout the year.

Can you identify a bee as a honey bee or a bumble bee? Practice image processing and classification techniques and help researchers seeking to protect pollinators from collapse.

Flag public health risks at restaurants by combining Yelp reviews with open city data on past inspections. An algorithmic approach discovers more violations with the same number of inspections.

Can you predict what drives women’s health care decisions in America? In an uncertain health landscape, this survey modeling challenge can help illuminate what care people receive, where they go, and how they pay.

Tag school budgets automatically to help districts get a better grasp of their spending and how to improve the impact of their scarce resources.