Overview
Geospatial Hackathon 2025 (GeoHack 25) is a flagship event jointly organized by APNNS, IEEE GRSS Kolkata Chapter, and IIIT Bhubaneswar, as part of the 9th International Workshop on Deep Learning and Artificial Intelligence (DLAI9). GeoHack 25 offers a dynamic platform for professionals, developers, engineers, and data scientists to showcase their skills in geospatial data analysis and machine learning. The hackathon aims to foster innovation by encouraging participants to develop creative, practical solutions to real-world problems in remote sensing and geoscience. With a focus on performance, sustainability, and real-life integration of geospatial technologies, the event brings together multidisciplinary talent to turn visionary ideas into impactful, functional prototypes.
This is an excellent opportunity for students to stimulate creativity, develop innovative projects, and address real-world challenges. The hackathon also encourages thought-provoking questions, broader perspectives, and supports participants in their journey toward career growth.
Stages & Timelines
Stage 1: Online Submission 7th Aug - 25th Aug 2025
Result Announcement: 31st Aug 2025
Stage 2: Offline Hackathon 6th Sep 2025
Final Event: 7th Sep 2025
Open to all branches and academic years.
Teams must consist of 2 to 3 members, per team.
All teams must submit a PPT for online submission
All Teams must submit a Prototype for offline round.
Submissions must be made before the deadline; late entries not accepted
Hackathon Format
Online Submission: Teams choose a problem statement from domains
from the given theme and submit the idea with a ppt.
Offline Round: To Present the Prototype with idea and result
Top 3 winners selected regardless of domain, based on innovation and excellence
Online Screening: Innovation, Feasibility, Impact, Clarity of PPT.
Offline Round: Feasibility, Industry Impact, Maintainability, Scalability.
Top 15 teams will be selected for offline round
Offline presentations/demos will be evaluated by industry experts and academic mentors
Problem Statement
Real-Time Disaster Detection and Response
Crop Health Monitoring and Yield Forecasting
Air Quality Index Prediction