ICLR Workshop Challenge #2: Radiant Earth Computer Vision for Crop Detection from Satellite Imagery
$5,000 USD
Identify crop type using satellite imagery, and win a trip to present your work at ICLR 2020 in Addis Ababa.
472 data scientists enrolled, 99 on the leaderboard
AgricultureComputer VisionSatelliteUnstructuredSDG2
3 February—29 March
55 days

Agriculture is the driving engine for economic growth in many countries, particularly in the global development community. Therefore, accurate and reliable agricultural data around the world is critical to global resilience and food security. These data are also essential to monitor the progress toward several UN Sustainable Development Goals, including ending poverty, zero hunger, economic growth and more.

Earth observations (EO) provide invaluable data at different spatial and temporal scales and at consistent frequencies. These data can be used to build models for agricultural monitoring, increasing farmers productivity and enhancing the impact of intervention mechanisms.

In contrast with a survey, agricultural maps based on satellite data provide a more accurate insight to stakeholders. While traditional data collection only provides aggregated information about regions as a whole—with statistical uncertainty due to regional limitations— Earth observations can provide data at scale with high spatial granularity.

The objective of this competition is to create a machine learning model to classify fields by crop type from images collected during the growing season by the Sentinel-2 satellite. The fields pictured in this training set are across western Kenya, and the images were collected by the PlantVillage team.

The dataset contains a total of more than 4,000 field images. The data you will have access to includes 12 bands of observations from Sentinel-2 L2A product (observations in the ultra-blue, blue, green, red; visible and near-infrared (VNIR); and short wave infrared (SWIR) spectra), as well as a cloud probability layer. The bands are mapped to a common 10x10m spatial resolution grid.

Western Kenya, where the data was collected is dominated by smallholder farms, which is common across Africa, and poses a challenge to build crop type classification from Sentinel-2 data. Moreover, the class imbalance in the dataset may need to be taken into account for building a model that performs evenly across all classes.

This competition is part of the Computer Vision for Agriculture (CV4A) Workshop at the 2020 ICLR conference and is designed and organized by Radiant Earth Foundation with support from PlantVillage in providing the ground reference data. Competition prizes are sponsored by Microsoft AI for Earth and Descartes Labs.

About the Computer Vision for Agriculture (CV4A) Workshop and ICLR (cv4gc.org):

Artificial intelligence has invaded the agriculture field during the last few years. From automatic crop monitoring via drones, smart agricultural equipment, food security and camera-powered apps assisting farmers to satellite imagery-based global crop disease prediction and tracking, computer vision has been a ubiquitous tool. The Computer Vision for Agriculture (CV4A) workshop aims to expose the fascinating progress and unsolved problems of computational agriculture to the AI research community. It is jointly organized by AI and computational agriculture researchers and has the support of CGIAR. It will be a full-day event and will feature invited speakers, poster and spotlight presentations, a panel discussion and (tentatively) a mentoring/networking dinner.

About The International Conference on Learning Representations (ICLR) (iclr.cc):

ICLR is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence called representation learning, but generally referred to as deep learning.

ICLR is globally renowned for presenting and publishing cutting-edge research on all aspects of deep learning used in the fields of artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, text understanding, gaming, and robotics.

Participants at ICLR span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

About Radiant Earth (radiant.earth):

Founded in 2016, Radiant Earth Foundation is a nonprofit organization focused on empowering organizations and individuals globally with open Artificial Intelligence (AI) and Earth observations (EO) data, standards and tools to address the world’s most critical international development challenges. With broad experience as a neutral entity working with commercial, academic, governmental and non-governmental partners to expand EO data and information used in the global development sector, Radiant Earth Foundation recognizes the opportunity that exists today to advance new applications and products through AI and machine learning (ML).

To fill this need, Radiant Earth has established Radiant MLHub as an open ML commons for EO. Radiant MLHub is an open digital data repository that allows anyone to discover and access high-quality Earth observation (EO) training datasets. In addition to discovering others’ data, individuals and organizations can use Radiant MLHub to register or share their own training data, thereby maximizing its reach and utility. Furthermore, Radiant MLHub maps all of the training data that it hosts so stakeholders can easily pinpoint geographical areas from which more data is needed.

About PlantVillage (plantvillage.psu.edu):

PlantVillage is a research and development unit of Penn State University that empowers smallholder farmers and seeks to lift them out of poverty using cheap, affordable technology and democratizing the access to knowledge that can help them grow more food.