Small farms (<2ha) produce about 35% of the world’s food, and are mostly found in low- and middle-income countries. Reliable information about these farms is limited, making support and policy-making difficult. Earth Observation data from satellites such as Sentinel-2, in combination with machine learning, can help improve agricultural monitoring, crop mapping, and disaster risk management for these small farms.
In this challenge, the goal is to classify crop types in agricultural fields across Northern India using multispectral observations from Sentinel-2 satellite. Fields are located in various districts in states of Uttar Pradesh, Rajasthan, Odisha and Bihar. The AgriFieldNet India training dataset is generated by the Radiant Earth Foundation, using the ground reference data collected and provided by IDinsight's Data on Demand team.
To be eligible for cash prizes, winners will release their top solutions under an open source license for ongoing use and learning. The winners’ solutions will be published on GitHub following Radiant Earth’s model repository template with proper documentation, and added to the Radiant MLHub model repository.
Enabling Crop Analytics at Scale
This challenge and the training data collection and curation is supported by a grant from the Enabling Crop Analytics at Scale (ECAAS). ECAAS Initiative is a multi-phase project that aims to catalyze the development, availability, and uptake of agricultural remote-sensing data and subsequent applications in smallholder farming systems. The initiative is funded by The Bill & Melinda Gates Foundation and implemented by Tetra Tech.
About Radiant Earth Foundation (radiant.earth)
Radiant Earth Foundation is a nonprofit corporation working to empower organizations and individuals with open machine learning (ML) and Earth observation data, standards, and tools to address the world’s most critical international development challenges. Radiant Earth fosters collaboration through a cloud-based open geospatial training data library, Radiant MLHub. Radiant also supports an ecosystem of practitioners to develop standards, expand interoperability around ML on Earth observation, and provide information and training to help advance the capacity of those working in the global development sector using ML and Earth observation. Visit Radiant Earth on Twitter, LinkedIn, Medium, and GitHub.
About IDInsight (idinsight.org)
IDinsight is a mission-driven global advisory, data analytics, and research organization that helps global development leaders maximize their social impact. We tailor a wide range of data and evidence tools, including randomized evaluations and machine learning, to help decision-makers design effective programs and rigorously test what works to support communities. We work with governments, multilateral agencies, foundations, and innovative non-profit organizations in Asia and Africa. We work across a wide range of sectors, including agriculture, education, health, governance, sanitation, and financial inclusion. We have team members who are remote and have offices in Dakar, Lusaka, Manila, Nairobi, New Delhi, and Rabat. The ground-reference data for this challenge was collected by IDInsight's Data on Demand team.
The evaluation metric for this challenge is Cross Entropy with binary outcome for each crop:
Your submission should include FieldIDs of the fields in the test dataset and the corresponding probability for each crop type. Your submission file should look like:
Field_ID Bersem Coriander Garlic ...
1184 0.14 0.10 0.41 ...
After code review, if you are in the top 5 on the leaderboard you will be requested to prepare a video to share with the host along with your code. Winners will be paired with a mentor to help prepare your code along with a 10-minute video presentation.
In the video you submit, you need to explain your approach to the problem as clearly as possible, including any relevant insights into the problem you discovered along the way (e.g. a clever way to engineer the raw features).
You will be judged on:
Final prizes will be awarded by the host, based on the above criteria.
1st Place: $5,000 USD
2nd Place: $3,000 USD
3rd Place: $2,000 USD
Competition closes on 31 October 2022.
Final submissions must be received by 11:59 PM GMT.
We reserve the right to update the contest timeline if necessary.
Teams and collaboration
You may participate in competitions as an individual or in a team of up to four people. When creating a team, the team must have a total submission count less than or equal to the maximum allowable submissions as of the formation date. A team will be allowed the maximum number of submissions for the competition, minus the total number of submissions among team members at team formation. Prizes are transferred only to the individual players or to the team leader.
Multiple accounts per user are not permitted, and neither is collaboration or membership across multiple teams. Individuals and their submissions originating from multiple accounts will be immediately disqualified from the platform.
Code must not be shared privately outside of a team. Any code that is shared, must be made available to all competition participants through the platform. (i.e. on the discussion boards).
The Zindi data scientist who sets up a team is the default Team Leader but they can transfer leadership to another data scientist on the team. The Team Leader can invite other data scientists to their team. Invited data scientists can accept or reject invitations. Until a second data scientist accepts an invitation to join a team, the data scientist who initiated a team remains an individual on the leaderboard. No additional members may be added to teams within the final 5 days of the competition or last hour of a hackathon.
The team leader can initiate a merge with another team. Only the team leader of the second team can accept the invite. The default team leader is the leader from the team who initiated the invite. Teams can only merge if the total number of members is less than or equal to the maximum team size of the competition.
A team can be disbanded if it has not yet made a submission. Once a submission is made individual members cannot leave the team.
All members in the team receive points associated with their ranking in the competition and there is no split or division of the points between team members.
Datasets and packages
The solution must use publicly-available, open-source packages only.
You may use only the datasets provided for this competition. Automated machine learning tools such as automl are not permitted.
If the challenge is a computer vision challenge, image metadata (Image size, aspect ratio, pixel count, etc) may not be used in your submission. You are not allowed to use the latitude and longitude of the pixels as predictors in your model either.
You may use pretrained models as long as they are openly available to everyone.
You are allowed to access, use and share competition data for any commercial,. non-commercial, research or education purposes, under a CC BY 4.0 license.
You must notify Zindi immediately upon learning of any unauthorised transmission of or unauthorised access to the competition data, and work with Zindi to rectify any unauthorised transmission or access.
Your solution must not infringe the rights of any third party and you must be legally entitled to assign ownership of all rights of copyright in and to the winning solution code to Zindi.
Submissions and winning
You may make a maximum of 10 submissions per day.
You may make a maximum of 300 submissions for this competition.
Before the end of the competition you need to choose 2 submissions to be judged on for the private leaderboard. If you do not make a selection your 2 best public leaderboard submissions will be used to score on the private leaderboard.
During the competition, your best public score will be displayed regardless of the submissions you have selected. When the competition closes your best private score out of the 2 selected submissions will be displayed.
Zindi maintains a public leaderboard and a private leaderboard for each competition. The Public Leaderboard includes approximately 20% of the test dataset. While the competition is open, the Public Leaderboard will rank the submitted solutions by the accuracy score they achieve. Upon close of the competition, the Private Leaderboard, which covers the other 80% of the test dataset, will be made public and will constitute the final ranking for the competition.
Note that to count, your submission must first pass processing. If your submission fails during the processing step, it will not be counted and not receive a score; nor will it count against your daily submission limit. If you encounter problems with your submission file, your best course of action is to ask for advice on the Competition’s discussion forum.
If you are in the top 10 at the time the leaderboard closes, we will email you to request your code. On receipt of email, you will have 48 hours to respond and submit your code following the Reproducibility of submitted code guidelines detailed below. Failure to respond will result in disqualification.
To be eligible for cash prizes, 1st, 2nd, or 3rd on the final leaderboard will release their top solutions under an open source license for ongoing use and learning. The winners’ solutions will be shared in a well-organized code repository with proper documentation, and added to the Radiant MLHub model repository.
If two solutions earn identical scores on the leaderboard, the tiebreaker will be the date and time in which the submission was made (the earlier solution will win).
If the error metric requires probabilities to be submitted, do not set thresholds (or round your probabilities) to improve your place on the leaderboard. In order to ensure that the client receives the best solution Zindi will need the raw probabilities. This will allow the clients to set thresholds to their own needs.
The winners will be paid via bank transfer, PayPal, or other international money transfer platform. International transfer fees will be deducted from the total prize amount, unless the prize money is under $500, in which case the international transfer fees will be covered by Zindi. In all cases, the winners are responsible for any other fees applied by their own bank or other institution for receiving the prize money. All taxes imposed on prizes are the sole responsibility of the winners. The top 3 winners or team leaders will be required to present Zindi with proof of identification, proof of residence and a letter from your bank confirming your banking details.Winners will be paid in USD or the currency of the competition. If your account cannot receive US Dollars or the currency of the competition then your bank will need to provide proof of this and Zindi will try to accommodate this.
Payment will be made after code review and sealing the leaderboard.
You acknowledge and agree that Zindi may, without any obligation to do so, remove or disqualify an individual, team, or account if Zindi believes that such individual, team, or account is in violation of these rules. Entry into this competition constitutes your acceptance of these official competition rules.
Zindi is committed to providing solutions of value to our clients and partners. To this end, we reserve the right to disqualify your submission on the grounds of usability or value. This includes but is not limited to the use of data leaks or any other practices that we deem to compromise the inherent value of your solution.
Zindi also reserves the right to disqualify you and/or your submissions from any competition if we believe that you violated the rules or violated the spirit of the competition or the platform in any other way. The disqualifications are irrespective of your position on the leaderboard and completely at the discretion of Zindi.
Reproducibility of submitted code
A README markdown file is required
It should cover:
Your code needs to run properly, code reviewers do not have time to debug code. If code does not run easily you will be bumped down the leaderboard.
Consequences of breaking any rules of the competition or submission guidelines:
Monitoring of submissions