OBSCURA | SATELLITE AI RESEARCH CHALLENGE

Observing Blocked Satellite Columns Using Remote-sensing and Auxiliary data

Can AI predict atmospheric pollution over industrial sites it has never seen?

Satellites measure the atmosphere every day, but clouds and retrieval limitations leave important gaps. OBSCURA challenges students to estimate five atmospheric measurements using Sentinel-2 satellite imagery, weather, terrain, and image-derived features.

OBSCURA is a student-led research challenge organized by BTAJI Crew, an AI and data science learning community. It combines rigorous machine learning with a real environmental monitoring problem, encouraging participants to experiment, validate their models, and communicate their research findings.

01 | THE RESEARCH CHALLENGE

Each sample represents an industrial facility during an observation window. Your task is to develop a machine learning model that predicts five atmospheric measurements using the provided imagery and environmental information.

Some training targets are blank because the corresponding satellite measurements were unavailable. A blank value is not zero. The evaluation ignores missing ground-truth measurements for each target while continuing to evaluate the available measurements.

What makes OBSCURA a research challenge?

The training and test datasets contain different industrial facilities from different time periods. Models must generalize to new locations and environmental conditions rather than memorize familiar sites.

Participants are encouraged to investigate different approaches, establish strong baselines, perform appropriate validation, compare experiments, and document their findings.

02 | HOW THE COMPETITION WORKS

OBSCURA uses Kaggle for the technical competition and Devpost for registration and final research documentation.

Step 1: Register on Devpost

Register for the OBSCURA hackathon on Devpost and review the eligibility requirements, rules, judging process, and prizes.

Step 2: Join the Kaggle Competition

Visit the official Kaggle competition, accept the participation rules, and explore the dataset and available resources.

Join OBSCURA on Kaggle

Read the Overview, Data, Evaluation, and Rules sections carefully before beginning.

Step 3: Develop Your Research Solution

Explore the starter notebooks, analyze the dataset, develop machine learning or deep learning models, perform experiments, and evaluate performance using appropriate validation techniques.

You may use Kaggle notebooks and available computational resources, subject to Kaggle's usage limits and competition rules.

Step 4: Submit Predictions on Kaggle

Generate prediction files in the required CSV format and submit them through Kaggle.

Kaggle manages the official technical evaluation and public and private leaderboards.

Step 5: Complete Your Devpost Research Submission

During the final submission week, complete your Devpost project and upload your research write-up as a PDF.

Your research report should explain your methodology, data preparation, baseline models, experiments, validation strategy, results, findings, and conclusions.

Include relevant Kaggle notebook or permitted GitHub repository links so your approach can be reviewed and reproduced.

Research write-up guidance and a completed example are available in the Resources tab.. Participants may organize their reports differently, provided all required information is included.

No participant presentation video is required.

Important: A valid Kaggle submission and a complete Devpost research submission are both required for prize and participation certificate eligibility.

03 | WHO CAN PARTICIPATE?

OBSCURA welcomes eligible students from recognized colleges and universities worldwide, subject to the official age, geographic, and platform requirements.

Participants may compete individually or in teams of up to 20 students.

Students from different universities may collaborate within the same team, subject to the official team formation and competition rules.

Team membership and participant information must be consistent across Kaggle and Devpost for eligibility verification.

04 | COMPETITION TIMELINE

All dates and times below are in Pakistan Standard Time (PKT, GMT+5).

Participants can work on their Kaggle models throughout the research period. The Devpost project submission window opens during the final week for research documentation and final project submissions.

05 | JUDGING AND EVALUATION

The official Kaggle private leaderboard determines the technical ranking using masked mean column-wise RMSE (MCRMSE). Lower scores indicate better prediction performance.

Final prize eligibility is subject to verification by the organizing team, including:

• Valid Kaggle submissions and final leaderboard results.
• Completed Devpost project and research PDF.
• Clear documentation of methodology and experiments.
• Reproducibility of the submitted solution where required.
• Student eligibility, correct team information, and compliance with competition rules.

The organizing team may disqualify entries that violate the competition rules or fail required verification checks.

06 | PRIZES AND CERTIFICATES

OBSCURA begins with an initial $50 USD cash prize pool.

Cash prizes are awarded per winning entry. An individual winner receives the corresponding prize. A winning team receives one prize for the team, not a separate cash award for each member.

The prize pool may increase if additional sponsors or supporters join. Any changes will be announced officially.

Digital Certificates of Participation

Every eligible student who completes the required submissions on both Kaggle and Devpost will receive an individual digital Certificate of Participation after verification.

Winning a cash prize or achieving a specific leaderboard position is not necessary to receive a participation certificate.

07 | COMMUNITY AND SUPPORT

Join the BTAJI Crew WhatsApp Community to receive announcements, ask questions, discuss technical challenges, and connect with potential teammates.

Join Our WhatsApp Community

After joining, open the community groups list and select the Kaggle group for OBSCURA-related discussions and updates.

Official dataset information, evaluation details, technical rules, and competition resources are available through the Kaggle competition page.

08 | FINAL NOTE

OBSCURA is more than a leaderboard competition. It is an opportunity to approach a real environmental monitoring problem through careful experimentation, scientific reasoning, and reproducible machine learning research.

Beyond the clouds. Beyond familiar sites. Build AI that generalizes.

Organized by BTAJI Crew.

• Kaggle Private Leaderboard Performance
  The final technical ranking is based on the OBSCURA Kaggle private leaderboard using the masked mean column-wise RMSE metric. Lower scores are better. Only eligible, valid, rule-compliant submissions are considered for awards.
• Research Quality & Reproducibility
  Judges review whether the written methodology clearly describes the model, preprocessing, training, validation, and results. Potential winners must provide sufficient code and documentation to reproduce their Kaggle submissions.
• Research Documentation & Rules Compliance.
  Judges review the Devpost project description and required research write-up PDF. They also verify student eligibility, matching Kaggle and Devpost teams, originality, reproducibility, and compliance with competition rules.