AI for Earth Program is sponsored by Microsoft. This grant program focuses on environmental sustainability and supports projects that utilize AI to address climate change, biodiversity loss, and water scarcity.

AI for Earth grantee gallery Microsoft AI for Earth grantee gallery Showcasing the work of Microsoft AI for Earth grant recipients Building a biodiversity atlas for northeast India The Ashoka Trust for Research in Ecology and Environment (ATREE) plans to use machine learning and computer vision to boost its efforts to map and catalog the unique, resource-rich ecosystem of northeast India.

Armed with detailed satellite images of the region and the AI for Earth grant, the team believes AI-enabled tools will help them create a comprehensive database of biodiversity to help policymakers and local communities make better-informed economic, ecological, and infrastructure-related decisions.

Aberystwyth University, To the Poles Gaining a better understanding of Earth’s melting glaciers Joseph Cook, a polar scientist from the United Kingdom, is applying machine learning to optical data from drones and satellites to explore the changing cryosphere of Arctic glaciers.

By training the algorithms to recognize how the different surfaces reflect certain wavelengths of light — wavelengths that can be measured by satellites as well as by drones — precision study of vast areas becomes feasible. After testing the algorithms on imagery from custom-built drones, Cook’s team will then apply them to satellite remote-sensing data, enabling them to scale up to entire glaciers.

Using AI to unleash the potential of urban agriculture AdaViv is developing an adaptive and efficient indoor growing system on Azure that uses sensors, actuators, and machine learning to monitor plant growth, predict yields, detect diseases, and understand precisely how nutrients, environment and light are affecting plant growth.

This system will help indoor producers attain higher yields, precise quality control, and hyper-efficient production. Creating smart, connected parks with cloud and AI Managing vast territories with dispersed endangered animal populations is a logistical feat, especially in regions with political instability and poaching.

For two decades, the nonprofit African Parks has served as steward for parks across Africa that would otherwise lack the resources to protect vulnerable animal populations. Cloud connectivity has transformed African Parks’ management approach from ad hoc interventions by roving rangers into a centralized command center from which the park manager can coordinate operations.

African Parks uses Microsoft cloud and AI tools to track elephants, detect threats to them, and coordinate an effective ranger response to protect the animals from poaching or illegal hunting. With machine learning, park managers also hope to better understand and even predict elephant behavior.

As low Earth orbit satellite constellations bring internet connectivity to the most remote regions, African Parks is poised to create smart, connected parks that are true sanctuaries for vulnerable endangered species like elephants, tigers, hyenas, and Kordofan giraffes.

Improving agriculture forecasting and conservation practices Professor Joshua Woodard launched Ag-Analytics, a service integrated with the John Deere Operations Center that provides intelligent, easy-to-use tools to help farmers plan and monitor their crops. Ag-Analytics brings together data from farm machinery sensors with other datasets such as weather and satellite imagery to develop models for yield and crop cover forecasting.

This information and more accurate forecasting can help shape policies to make it economically feasible (through insurance subsidies) for farmers to implement conservation practices. Accelerating innovation in agri-food by linking data The agri-food sector is one of the least digitized industries in the world, with many barriers to collecting, sharing, and using data.

Agrimetrics was founded to accelerate innovation in the agri-food industry by connecting data and thus enabling advanced analytics and AI. Their goal is to help agri-food businesses produce food more efficiently and sustainably. As a long-time Microsoft Partner, Agrimetrics uses Microsoft Azure technology to power an agri-food Data Marketplace.

This Data Marketplace lets data providers, from farmers to global corporations, market and manage their data, and helps data consumers, from researchers to businesses and government organizations, to find and use that data.

Agrimetrics is now working with the AI for Earth program to forge new collaborations, such as with other AI for Earth grantees, and extend its capabilities to deploy innovative, sustainable, and scalable solutions to environmental and agricultural problems around the world.

Solving the challenge of increasing flood frequency and severity in developing countries Michael Souffront, a software engineer at Aquaveo, developed a high-density hydrologic model and visualization tool for forecasting global floods. The GloFAS-RAPID model produces streamflow forecasts not just from major rivers, but also medium-sized and smaller streams — helping advance flood preparedness, especially in developing countries.

Applying AI to improve the accuracy of air quality measurements Air pollution is the single biggest environmental health threat of our time, killing 7 million people and costing the world economy USD5 trillion per year.

Data-driven decision-making around air pollution mitigation has been unfeasible, as traditional sensing equipment is expensive, stakeholders lack necessary knowledge to analyze the data, and suitable interventions are difficult to define.

Breeze Technologies aims to deliver hyperlocal comprehensive and accurate air quality data from public and private data sources and low-cost sensors, as well as insights based on recent scientific studies and actionable recommendations from a growing, self-learning catalog of more than 3,500 air quality interventions.

Carnegie Mellon University Countering poaching with adaptable AI Poaching is one of the greatest threats to wildlife conservation and is very difficult to prevent. Rangers have had to develop their own skills and intuitions through years of field experience, and they lacked modern technological tools that could help them make better decisions.

PAWS, developed by Dr. Fei Fang of Carnegie Mellon University, is an AI tool that uses machine learning and behavior modeling to help rangers plan more effective patrol routines. Through a Microsoft AI for Earth grant, Dr. Fang took the next step in developing PAWS by adding real-time interactive tools that can take new information from the rangers on patrol and offer updated strategies for tracking down poachers.

Using spaceborne sensors and AI to measure forest biomass In the wake of Glasgow COP26, world leaders are aligned in their commitment to reduce atmospheric carbon and keep global warming below the critical 1. 5°C threshold. Making good on this promise requires changing the way forests and landscapes are managed.

Microsoft Planetary Computer partner Chloris Geospatial has developed a unique technology that goes beyond monitoring forest cover—the traditional monitoring approach—to measure directly the growth and degradation of above-ground biomass over time, providing accurate, global measurements of a crucial component of earth’s carbon stock.

Chloris’ proprietary solution uses satellite data and machine learning to transform how forests and other ecosystems are monitored. At 30-meter resolution, it offers accurate insight into changing carbon stock at the global, national, regional, and even the project level.

City University of New York Furthering oceanographic study with the Microsoft cloud The Ocean Observatories Initiative (OOI) Cabled Array collects large quantities of data from the seafloor and overlying ocean environment of the Juan de Fuca tectonic plate in the northeast Pacific — providing a valuable opportunity for researchers and students to learn more about the ocean and seafloor processes.

But currently, downloading and processing the data on local computers takes days or even weeks. With funding from the Microsoft AI for Earth program, Dr. Timothy Crone and Dr. Dax Soule are helping make this data more accessible and usable to scientists and students around the globe by building a Microsoft cloud-based system on Pangeo, an open-source platform for big data geoscience.

Increasing access to technology is a passion for Dr. Soule. At the City University of New York (CUNY), he teaches students from very diverse ethnic and economic backgrounds that are often not well-represented in science-related careers.

Through the partnership with Microsoft, Dr. Soule and his students now have the cloud-based tools they need to access and work with the OOI Cabled Array data, conducting important research and becoming the next generation of oceanographic scientists.

CoCoRaHS Network, Colorado State University Enhancing climate data and research with AI Precipitation can vary a lot over surprisingly small distances, as demonstrated by the Spring Creek flood in Fort Collins, Colorado, in 1997, when 14. 5 inches of rain fell in a highly concentrated area and caused a deadly flash flood in nearby neighborhoods that had significantly less rain.

From that disaster was born the Collaborative Community Rain, Snow, and Hail Network — CoCoRaHS for short — which works with thousands of volunteers to gather daily data on precipitation.

CoCoRaHS provides small-scale coverage that helps weather services issue timely alerts on severe weather conditions that can save lives, and its accumulated records also help other organizations, from climatology to agriculture, engineering, and insurance, with long-term planning.

Now thanks to a Microsoft AI for Earth grant, CoCoRaHS is improving the quality of its reports through AI, pulling more information out of the reports with natural language processing, and making that data more available through Azure Notebooks and Power BI.

Keeping a close watch on our forests, for our future Professors Tian Zheng and Maria Uriarte at Columbia University are using ground observations of forest plots to create a machine learning pipeline that’s capable of correctly classifying the species of individual trees using aerial photographs and LiDAR data collected by NASA using remote sensing technologies, in order to better understand how storms affect a forest’s ability to store carbon and aid in climate change mitigation, and how damaged forests recover over time.

Closing the gap between field work and analysis Conservation Metrics is developing automated solutions using Microsoft Azure that collect, process, and analyze terabytes of wildlife data. By moving its infrastructure to Azure, Conservation Metrics hopes to give researchers more time and resources to meet their conservation goals by significantly closing the gap between field work and information and discovery.

Conservation Science Partners Protecting forest and water resources with AI Forests in the western United States are suffering increasing tree losses due to several causes, including droughts from climate change, wildfires, and beetle infestations.

This loss of trees is a serious problem not only for maintaining carbon storage, but also for the availability of water resources, as forests play an important role in replenishing local watersheds. A regional-scale analysis of forest disturbances and their impact on water resources changes is necessary to better understand and manage these issues.

With recent advances in AI, machine learning, and cloud computing, it’s now possible to combine satellite imagery at medium and high resolutions and analyze this data to see how the forest cover across the region changes from disturbance events. Additionally, that analysis can be correlated to water supply records to understand those impacts as well.

Through the insights gained from this study, local communities, regional organizations, and the federal government can better manage and protect these vital resources.

Cornell University Center for Conservation Bioacoustics Monitoring insect sounds in tropical rainforests Led by Holger Klinck and Laurel Symes of Cornell University’s Bioacoustics Research Program, a team of researchers is looking to AI-powered acoustic monitoring of insects as a way of better understanding the dynamics of rainforest habitats.

The team is focusing first on neotropical rainforest katydids, a diverse group that occupies a central position in tropical food webs. How the wide variety of katydids interacts with the rest of the forest species, both plants and animals, can provide lots of information about the overall ecosystem.

Klinck aims to scale beyond insects to other species, including birds, monkeys, and other vocal animals, to help advance conservation of tropical rainforests.

Improving crop water efficiency in Uganda At DHI GRAS, Dr. Torsten Bondo and Dr. Radoslaw Guzinski — a small Denmark-based company focused on Earth observation and satellite imaging — are using machine learning and satellite remote sensing to measure the rate of water evaporation from soil and plant surfaces into the atmosphere from fields.

Their goal is to help Ugandan farmers reduce water use by knowing more precisely how much water their crops really need. Classifying land cover to improve maps of landslide susceptibility, water, and carbon storage Due to technological advances, scientists can now capture data on Earth and off-world at rates that greatly exceed the ability to interpret it.

These massive datasets challenge the delivery of timely maps and analysis to the nation. Artificial intelligence (AI) tools, including machine learning, allow us to rapidly interpret these data sets to solve national challenges. Digamma.

ai and the U.S. Geological Survey’s National Innovation Center (USGS NIC) are using machine learning to dramatically improve land cover models, with the intent of improving maps of landslide susceptibility, water, and carbon storage. In addressing this challenge, Digamma.

ai used geologic mapping and USDA imagery to train machine learning models to discriminate between bare rock and exposed soil, improving land-cover maps across the Sierra Nevada in California. Dr. Monique Mackenzie, University of St.

Andrews Saving endangered vultures through AI modeling Vultures perform essential ecosystem services by scavenging on dead animals, which is crucial in preventing the spread of disease to other animals and humans. However, deliberately poisoned carcasses—a result of human-wildlife conflict—can result in several hundred vulture deaths at a single poisoned carcass. Dr. Monique Mackenzie, a statistician and Provost at the University of St.

Andrews in the United Kingdom, is working with a team in Namibia to stop the decline of the vulture population. By analyzing the locations and activity of GSM/satellite tagged animals which locate carcasses as part of normal foraging behavior, the team can quickly locate and attend to the carcasses, preventing many deaths. Through an AI for Earth grant, Dr. Mackenzie can help the team upscale their solution and create lasting impact.

Monitoring climate change in the Antarctic with machine learning Climate change is disrupting the pristine ecosystem around the western Antarctic peninsula, a globally significant center of biodiversity based around the presence of krill that provide sustenance for many other species. Many of the world’s whales spend their summers here as their primary feeding grounds.

By monitoring the size and health of the whales, it’s possible to gain insights on the abundance of krill and the ecosystem as a whole. Satellites and drones now enable vast amounts of image and video data on the whales to be collected — more than could ever be efficiently processed by people.

The Duke University Mobile Robotics and Remote Sensing Lab is developing machine learning models on Microsoft Azure that can manage this massive data and quickly provide the statistics needed to help further research and environmental protection efforts. Making these models available as APIs on Azure will also enable other researchers to improve their work.

Fighting deforestation with deep learning and smart contracts Deforestation is one of the significant contributors of greenhouse gases and drivers of climate change. Much of that deforestation comes from local farmers trying to make their living, which presents the possibility of combating it by offering financial incentives to preserve the trees.

In many areas, such as the Amazon forest, it would be too time-consuming and difficult to determine who has the legal claim to be the caretaker for a particular section of forest. AI researcher David Dao and his team came up with an innovative alternative: help make everyone a caretaker of the forest by letting anyone put a financial stake in its well-being and earning a higher repayment when it is conserved.

Through AI technologies on Microsoft Azure, including machine learning and blockchain-enabled smart contracts, Dao was able to make this concept, GainForest, a reality. Tracking diseases through scientific literature with AI As new infectious diseases emerge and spread in different areas of the world, tracking the outbreaks is an important step in analyzing where they might emerge or spread next.

Archives of scientific publications such as PubMed Central present a resource for monitoring this information, but with thousands and thousands of articles published annually without a common standard for presenting data, extracting that data is very challenging.

EcoHealth Alliance, an international nonprofit organization dedicated to preventing pandemics and protecting both human lives and wildlife, is turning to AI to meet this challenge.

With assistance from a Microsoft AI for Earth grant, EcoHealth Alliance is developing PubCrawler, an AI-based software project that uses natural language processing to produce high-resolution datasets of the locations where research is being done into various diseases. The tools of this project also can be applied more broadly to meet other needs in biodiversity and conservation research.

Optimizing coffee harvesting with AI Coffee farming is a financially risky effort because the coffee berries ripen at varying rates even on the same tree branch, making it challenging to avoid a wasteful amount of underripe or overripe fruit. Climate change is increasing that risk by reducing yields and quality, increasing pests and diseases, and even making farmlands untenable.

These changes are not merely an inconvenience for coffee drinkers around the world, but represent a serious threat to the livelihood of tens of millions of small-scale farmers in developing nations. Farming Online is working to mitigate that risk by enabling farmers to harvest a higher proportion of fully ripe coffee.

Through a Microsoft AI for Earth grant, the Farming Online team is developing a machine learning model and smartphone app that will let farm workers in the field use photos of the coffee berries and current weather data to predict the best time to harvest. Unlike the familiar honeybees which live together in hives, most bee species are solitary and therefore difficult to study.

These solitary bees also play a far greater role in pollination than is commonly known, and understanding their lives is important to managing biodiversity and conservation efforts. For that purpose, Dr. Ariane Harrison and her team at Harrison Atelier created the Pollinators Pavilion, a prototype field station and educational tool that provides an artificial habitat and monitoring station for 2,000 solitary bees.

Using automated cameras and machine learning analysis, the Pavilion will help researchers better study the bees, while also providing a means for the public to learn more about these important pollinators as well. Understanding the risk of sea level rise to populations Sea levels are rising and the impact on coastal communities will be far reaching.

Though most communities are aware and conceptually understand what it could mean to local infrastructure, very few are focusing on addressing the inevitable challenges this will bring to individuals.

HighTide Intelligence is quantifying the financial impact of climate-driven flooding with the goal of preparing people and cities to understand the risks and measures required to adapt to changing climates Developing an intelligent tool for monitoring monkey populations Ankita Shukla’s and a team at IIIT Delhi are developing an intelligent tool to monitor and control rapidly growing urban monkey populations.

The tool will use Microsoft cloud and AI tools to detect and identify individual monkeys from images captured by photographers and camera traps, helping researchers identify and find monkeys needing sterilization and distribute contraceptive-laden food. Enabling equitable water distribution to residents in megacities The vastness of the Amazon means that identifying and understanding trends there can be a daunting task.

While images were available for processing, the initial process was highly manual and time consuming. And while image processing computer models was available, the sheer volume meant that the computing power required was significant. Using Microsoft Azure, image recognition became scalable to larger geographies in order to locate and predict fires and deforestation.

This prediction allows governments and NGOs to better inform their budgets and allocate resources. Indian Institute of Science Enabling equitable water distribution to residents in megacities Dr. Yogesh Simmhan is part of an interdisciplinary team that is applying their experience with the Internet of Things to the challenge of water management in megacities.

As part of the EqWater project, Dr. Simmhan will use data analytics and machine learning to identify inequities in water distribution and develop data-based recommendations to resolve them.

International Center for Tropical Agriculture (CIAT) Using AI to prevent malnutrition in sub-Saharan Africa Dr. Mercy Lung’aho and the International Center for Tropical Agriculture are tackling the issue of chronic malnutrition in sub-Saharan Africa with NEWS, a Microsoft AI-powered diagnostic model designed to predict and prevent a nutrition crisis before it occurs.

NEWS will aggregate and analyze satellite imagery and traditional data, such as rainfall, temperature, and vegetation health, to help predict the nutritive value of crops. Insights from NEWS will then help inform interventions to boost nutrition in sub-Saharan Africa.

International Crops Research Institute for the Semi-Arid Tropics ICRISAT Helping solve the big challenges of small farmers Dr. Mamta Sharma and a team at ICRISAT are using Microsoft cloud computing and AI together with IoT sensors to help with real-time monitoring of small farms in developing countries and provide pest diagnosis and farm and market advice to farmers through an AI-supported mobile application that displays personalized prediction results and recommend actions for each farmer.

Assessing the impacts of development in Uganda The wilderness of the Murchison Falls National Park and nearby Lake Albert in Uganda is threatened by development for oil production. However, the potential impact is difficult to assess without knowing what changes are happening to the land.

Through a Microsoft AI for Earth grant, Ketty Adoch will be applying machine learning to analyze aerial imagery of the landscape, tracking the changes in the previous and upcoming decades. These algorithms and analyses will support conservation efforts going forward.

Leadership Counsel for Justice and Accountability Forecasting regional-scale water shortages Residents in California’s rural Central Valley often rely on private domestic wells for drinking water, but many of these wells are vulnerable to failure when groundwater levels fall due to drought or unsustainable management.

In fact, for the past century, Californians have consumed more water in any given year on average than has been naturally replenished in aquifers.

Using historical groundwater level data, Leadership Counsel for Justice and Accountability and UC Davis work to predict groundwater level trends; this output is then lined up with the state’s Well Completion Report database, which shows the location, depth, and type of wells (agricultural, public supply, or domestic).

Algorithms are applied to determine how vulnerable each well is to failure, based on both pump location and local groundwater levels. Lion Identification Network of Collaborators Counting lions through AI for conservation To protect a threatened species, we need to know how many animals are left and where they are. That can be extremely difficult to determine for species that are wide-ranging and very similar-looking, such as lions.

The Lion Identification Network of Collaborators (LINC) is working to provide a collaborative online database to help researchers overcome this problem. Through a Microsoft AI for Earth grant, LINC is developing AI techniques to identify individual lions through images with far greater accuracy than humans could manage. With that capability, lions can be more easily tracked, managed, and protected.

Using the power of Azure to save salmon in the Salish Sea Long Live the Kings is developing an ecosystem model on Microsoft Azure to answer critical questions facing salmon recovery and sustainable fisheries in the Salish Sea. On Azure, researchers can run up to ten simulations at a time and get results in days instead of weeks — propelling research that informs ecosystem management and policy decisions.

Lower Atmosphere Research Group Improving short-term forecasting with radar analysis Weather forecasting is notoriously difficult. So many factors play into predicting what is going to happen in the earth’s atmosphere.

Jennifer Davison, President of the Lower Atmospheric Research Group, is taking advantage of existing Next-Generation Radar data (NEXRAD) measurements to map out the mean mesoscale, real-time vertical structure of moist, dry, and other significant layers to improve short term forecasting and our knowledge of the lower atmosphere.

Protecting bird populations after hurricanes with AI-enabled monitoring Increasingly intense storms are causing significant erosion and land cover change along US coastlines, resulting in critical habitat loss for birds.

The National Audubon Society is using Microsoft cloud and AI tools to improve bird monitoring after weather-related disasters, helping researchers quickly assess disturbance effects and act to preserve endangered coastal birds.

National Geographic Labs, Conservation Intelligence Moving conservation platforms to the cloud National Geographic Labs recognized that the conservation model practiced for the last several decades is failing and began exploring new ways to advance their efforts.

In the conservation sector, the same model has been followed without incorporating learnings from mistakes, and there will unfortunately always be more poachers than rangers to detect them.

National Geographic Labs asked itself how other industries have benefited from technology and why it’s been failing in the conservation sector, recognizing that there was a disconnect between folks in the bush and the people with technology know-how. In order to more efficiently use technology to advance conservation, the two groups needed to understand how to collaborate and communicate with each other.

Using AI, technology can help conservation workers recognize events that normally require hands-on continuous observation to identify. Technology acts as a force multiplier to help the people on the ground be more efficient at their jobs and to better protect the animals and places that they’re tasked with protecting.

While technology is not a silver bullet, it’s a critical component in making conservation successful moving forward. National University of Ireland Galway Saving the bees with sustainable farming – and AI Honeybees are one of the most widely used pollinators, playing a vital role in maintaining the world’s food supply.

However, bee populations have declined dangerously, and modern intensive agriculture is one of the main causes — including pesticide use and monoculture crop production. Agustin Garcia Pereira saw these problems firsthand, growing up in a farming community in Argentina.

Now, as a software engineer and researcher with the Insight Centre for Data Analytics at the National University of Ireland Galway, he is using remote sensing data, Microsoft Geo AI Data Science Virtual Machines, and GIS mapping to develop machine learning models that can identify agricultural practices at field level across wide areas.

This information will help farmers, beekeepers, and governments shift to more ecological and sustainable agriculture that also helps sustain the bees. Building a unique tool to map high-priority conservation areas NatureServe is developing an unparalleled tool for identifying the places most critical for conserving at-risk species in the contiguous United States.

With support from Esri, The Nature Conservancy, and Microsoft, NatureServe and its network of state natural heritage programs are applying machine learning techniques to their comprehensive biodiversity inventory data to model habitat for more than 2,600 at-risk, taxonomically and ecologically diverse species.

These spatial models will be synthesized into a map that identifies high-priority biodiversity conservation areas — a dynamic, transparent, and repeatable base layer to help guide effective conservation decision-making. Curbing illegal fishing with satellite data and AI Illegal, unreported, and unregulated fishing have significant detrimental impacts on biodiversity and exacerbate ocean impacts of climate change.

Healthy and productive ocean ecosystems are necessary for human food security, livelihoods, and health, and for helping the planet be more resilient in the face of climate change. OceanMind is working to increase the sustainability of fishing by analyzing vessel movements and identifying their behavior and regulatory compliance.

This helps governments enforce existing laws more effectively and helps seafood buyers make more responsible choices. Through a Microsoft AI for Earth grant, OceanMind will move its data analytics to the Microsoft Azure cloud, allowing it to analyze more data in real time, faster and more accurately. That will greatly improve OceanMind’s ability to help in the fight against illegal fishing.

Patagonian Institute for the Study of the Continental Ecosystems Automating the mapping of land use and land cover The Chubut watershed, located on the arid Patagonian steppe, is the main source of water for 250,000 people. Due to climate change, water yield is expected to decrease by an estimated 20 to 40 percent in the Chubut River by the end of the century.

Since 2018, Dr. Ana Liberoff of the Patagonian Institute for the Study of the Continental Ecosystems and her colleagues have worked to model the impacts of human practices on water quality and quantity. An important input for modeling human impacts are land use and land cover (LULC) maps.

Deep learning neural network algorithms can facilitate standardized methods for producing consistent LULC maps, allowing for more accurate tracking of changes over time. The AI for Earth Innovation grant, a partnership between the Microsoft AI for Earth program and The National Geographic Society, is helping Dr. Liberoff’s team to automate LULC map production using a transdisciplinary approach.

By combining remote sensing data and vegetation indices, the team will produce maps that can then be validated by stakeholders on the ground and used to predict future changes to land and water use. Connecting farmers in Africa to conservation practices For many years, conservation farming has been taught and implemented in marginal areas of trans-frontier conservation areas in southern Africa.

The Peace Parks Foundation is extending these practices on a larger scale by offering mobile devices with a custom app that helps farmers learn the methodology and measure the process.

Fighting wildlife crime with intelligent poacher detection To combat increasing wildlife crime, the Peace Parks Foundation is developing Smart Park, an integrated set of systems and technologies on Microsoft Azure designed to significantly enhance anti-poaching methods and protection for rhinos and other endangered wildlife by providing data-driven and intelligent decision-making.

Perpetuating the traditional skills of animal tracking Traditional animal tracking skills offer value to modern needs for conservation, wildlife protection, and ecotourism. However, these skills are in danger of being lost. Peace Parks Foundation has been involved with the SA College for Tourism’s Tracker Academy for several years.

The Tracker Academy seeks to restore these skills, and now bring them into the modern age with a custom app that helps students learn to track, while also teaching the general populace the value of tracking. Supporting sustainable livelihoods for the world’s cocoa farmers Climate change is threatening the livelihoods of 5 million smallholder cocoa farmers who produce roughly 90 percent of the world’s cocoa supply.

The Rainforest Alliance is using its Microsoft AI for Earth grant to develop a machine learning model to predict cocoa yield and a customized digital dashboard in ArcGIS Pro that will help farmers optimize their agricultural practices and improve their incomes