Leaflet / Mapbox
Browse and strategize across all 76 official problem statements requiring Leaflet / Mapbox. Complete with verified datasets, tech stack blueprints, and jury defense playbooks.
Problem Statements List (76)
Match My TeamAI-Based early warning and landslide Risk Monitoring System in NER
Landslides, flash floods, and slope failures in the North Eastern Region isolate hill communities and delay disaster response due to purely reactive manual reporting. Build an AI-driven early warning platform that ingests rain gauge telemetry, soil moisture sensors, and satellite slope data to broadcast real-time localized warnings via SMS and offline-capable PWA to village administrations.
Al-Based Smart Logistics and Accessibility Intelligence Platform for North Eastern Region (NER)
Difficult mountain terrain and frequent road blockages across the North Eastern Region delay delivery of essential medicines, food supplies, and construction materials. Build an AI route-optimization and accessibility platform that dynamically tracks convoy movement, predicts weather disruptions, and recalculates alternate supply corridors.
Safe and Efficient Operation of Mine Vehicles in Fog and Low-Visibility Conditions in Open Cast Iron Ore Mines.
Monsoon fog at NMDC's Bailadila iron ore mines reduces haul road visibility to 3-5 meters, causing heavy dumpers to halt operations and costing millions in lost ore production. Build an edge-AI collision avoidance and situational awareness platform that fuses thermal imaging, LiDAR/radar proximity telemetry, and high-precision GPS haul road digital twin mapping.
Belt Joint Rupture and Conveyor Belt Damages in Iron Ore Mining Industry: Intelligent Monitoring and Prediction of Conveyor Belt Joint Rupture and Damages in Iron Ore Mining Industry.
Continuous iron ore conveyor belts at NMDC mines suffer sudden joint splice ruptures and belt tears from heavy loads and abrasive dust, causing catastrophic production halts and safety hazards. Build an IoT and AI predictive maintenance system that analyzes high-speed vision camera feeds, thermal splice temperatures, and acoustic vibration telemetry to detect micro-cracks before splice failure occurs.
Using AI/ML and Space Technology to Identify Manganese Reserves and Overcome Production Shortfalls.
MOIL Ltd. relies on time-consuming manual core-drilling logs and historical records to estimate manganese reserves and schedule mine production, leading to unexpected tonnage shortfalls. Build an AI/ML space-assisted mining dashboard integrating satellite multispectral surface indices (vegetation anomaly, land surface temperature, soil moisture) with geological borehole data to map underground manganese ore veins and predict production bottlenecks.
Survey/Resurvey of Rural Agricultural Land in lndia
Decades-old manual chain-and-tape rural land surveys have caused widespread cadastral boundary errors, unrecorded informal land partitions, and high civil court litigation across rural India. Build a modern digital resurvey platform integrating RTK-DGPS ground control points, high-resolution drone orthomosaics, and automated vector parcel snapping to modernize Record of Rights (RoR) and resolve boundary overlaps.
3D ULPIN Generation and vertical Property Mapping SYstem
Conventional 2D land records cannot represent multi-storey apartments, underground metro utilities, or elevated flyovers, leading to ambiguous property ownership and legal friction in growing cities. Build a volumetric 3D Cadastre and 3D ULPIN (Unique Land Parcel Identification Number) engine that converts architectural BIM/floor plans and LiDAR point clouds into georeferenced 3D volumetric property parcels.
AI-Based Automated Urban Parcel Mapping and Cadastral Feature Extraction System using Drone lmagery
Manual parcel delineation and field ground-truthing in dense Indian urban settlements is exceptionally slow due to complex building footprints, narrow alleys, and irregular encroachments. Build an AI automated cadastral feature extraction platform that processes high-resolution drone orthophotos to segment building footprints, vectorize parcel property lines, and validate topological consistency.
Automated lntegration and lntelligent Harmonization of Multi-source Geospatial Data for urban Land Record Management.
Urban land records in India are scattered across incompatible departmental silos—revenue records, municipal GIS tax layers, utility pipe networks, and drone surveys all have conflicting coordinates and schemas. Build an AI-driven geospatial harmonization platform that automatically geo-references, conflates, and topologically syncs multi-source spatial layers into an authoritative cadastral database.
An lntegrated GIS-based Digital Public lnfrastructure for Land Governance
Land administration in India is fragmented across 28+ states with completely disparate data schemas, record formats, and administrative workflows. Build 'Land Stack'—an open, modular Digital Public Infrastructure (DPI) for land governance that unifies georeferenced cadastral base layers (ULPIN), essential ownership rights (RoR/registration/master plans), and citizen services via standardized open APIs.
Application of Geospatial Techniques for visualization and analysis to interpret Geo-Coded lmages to enhance watershed Development Outcomes.
Watershed development monitoring in rural India relies on manual documentation while thousands of geotagged field photos remain unlinked to satellite observations. Build an integrated geospatial watershed analytics platform combining 30-meter SRISHTI-DRISHTI / Landsat satellite imagery with field-collected geocoded photos to track water conservation check-dam impacts, vegetation greening (NDVI), and soil moisture changes over time.
Real-Time National Land Acquisition & Management System for End-to-End Digital Monitoring and Decision Support
National infrastructure projects (highways, railways, industrial corridors) suffer massive delays and cost overruns due to disconnected manual land acquisition tracking across central ministries, states, and district collectors. Build a real-time National Land Acquisition & Management portal providing end-to-end digital lifecycle workflows—from Section 4 notifications and social impact assessments to direct benefit transfer (DBT) compensation disbursement and possession handover.
Predictive Analytics System for Early Detection of Land Acquisition Delays
Infrastructure projects in India experience unforeseen land acquisition delays averaging 18 to 36 months, inflating project budgets without early warning. Build an AI predictive decision-support system analyzing historical project variables (litigation rates, compensation gaps, forest clearances, district administrative throughput) to forecast delay probabilities at each stage of the acquisition lifecycle and recommend targeted mitigation actions.
Intelligent Land Record Digitization and Validation System
Centuries of historical Indian land records exist in fragile handwritten registers, faded Urdu/Modi/Devanagari scripts, and degraded scanned PDFs that conventional OCR cannot read. Build an intelligent document processing platform combining multilingual vision-language models (VLM/OCR), layout segmentation, and automated revenue rule validation to extract structured Khasra/Khata landowner records and flag forgery or clerical errors.
National Digital Platform for Research, Policy Innovation, and Evidence-Based Land Governance
Land governance research, policy evaluation, and geospatial datasets in India remain locked in institutional silos, preventing policymakers from simulating reforms before enacting legislation. Build an AI-enabled National Land Research & Policy Innovation platform featuring an open data repository, interactive GIS policy impact visualizers, and macro-simulation sandboxes to evaluate land reform policies (e.g. tenancy formalization, agricultural zoning, urban conversion).
AI-Based Smart Governance and Compliance Monitoring System for Coal Mines
Coal India subsidiaries manage safety inspections, DGMS statutory compliance, environmental clearances, and contractor worker attendance across hundreds of open-cast and underground mines using disconnected spreadsheets and delayed paperwork. Build a centralized AI smart governance and compliance monitoring platform with mobile geotagged field inspection logging, automated statutory deadline escalation, and predictive violation heatmaps.
Development of an AI-enabled Low Cost Real Time Mine Subsidence Monitoring, Prediction and Early Warning System for Underground Coal Mines in India
Underground coal mining causes unexpected surface ground subsidence, damaging roads, railway tracks, and rural villages located above active extraction panels. Build an indigenous, low-cost real-time subsidence monitoring platform using a wireless surface mesh sensor network (ESP32 + LoRa/Zigbee) measuring ground tilt, extensometer displacement, and micro-vibrations to forecast ground collapse before catastrophic failure.
Development of Mobile (Quadruped)/Handheld Device/System for Real-Time Detection of Narcotics and Explosives across Indian Railways.
Drug trafficking and explosive threats across Indian Railways platforms, train coaches, and ballast tracks require rapid non-intrusive screening in GPS-denied, crowded environments. Build an AI-enabled autonomous quadruped robot and handheld inspection system equipped with thermal/optical cameras, LiDAR SLAM, and chemical spectrometry/sensor payloads to detect narcotics (heroin, cocaine, meth) and high explosives (RDX, TNT, IEDs).
Dynamic Forecast of Expected Time of Arrival (ETA) for Coaching Trains
Indian Railways National Train Enquiry System (NTES) uses static historical section running times to forecast train Arrival Times (ETA), leading to inaccurate passenger updates during unexpected yard congestion and weather delays. Build a real-time dynamic ETA forecasting engine combining GPS locomotive telemetry, section signaling occupancy, line capacity bottleneck models, and weather feeds to predict station arrival times with minute-level precision.
Adaptive Path Planning and Collision Avoidance for Autonomous Vehicles on Unstructured Indian Roads
Autonomous vehicles navigating complex Indian traffic environments face dynamic obstacles (pedestrians, two-wheelers, stray animals) and erratic road geometry that static path planners cannot handle. Build an adaptive path planning and collision avoidance system in MATLAB/Simulink and ROS2 integrating Model Predictive Control (MPC), dynamic obstacle velocity tracking, and Frenet frame trajectory generation for safe autonomous navigation.
Al-Powered Underground Mine Safety, Monitoring and Rescue System.
Underground coal and metal miners in Jharkhand face mortal risks from hazardous toxic gas build-up (methane CH4, carbon monoxide CO), roof falls, and oxygen depletion with zero real-time wireless communication to surface rescue teams. Build an AI-powered underground mine safety and rescue monitoring system using intrinsically safe IoT sensor nodes, underground mesh communication, and a digital twin tracking miner vitals and toxic gas plumes.
Portal for Academia - Industry collaboration for Skill Mapping, Internships and Placement
Ayush academic institutions (Ayurveda, Yoga, Unani, Siddha, Homeopathy) and pharmaceutical/wellness industries operate in silos, leading to skill mismatches, lack of verified clinical internships, and uncommercialized research patents. Build a dedicated Academia-Industry Ayush Collaboration Portal featuring AI skill mapping, verified clinical internship matching, industry R&D problem statements, and patent technology licensing showcases.
Modifications to improve the reliability, efficiency,and lifespan of electrical and electronic equipment and systems in the ambient condition of subzero temperature and low pressure of High Altitude Areas(HAA) and Super High Altitude Areas (SHAA) of Ladakh region.
Unmanned Aerial Vehicles (UAVs) and drones deployed in extreme desert and border regions face rapid motor overheating, bearing wear, and battery degradation under high ambient dust and temperature. Build a predictive digital twin and hardware telemetry analysis platform that ingests ESC telemetry, motor vibration FFT signatures, and thermal sensors to forecast drone subsystem failures and optimize propulsion lifespan for DRDO.
High Altitude Performance Optimization and Robust Design of Anti-Drone System.
Anti-drone jammers and directed energy counter-UAS systems deployed at high-altitude Himalayan forward posts (Ladakh/Sikkim >14,000 ft) suffer severe RF power amplifier overheating, sub-zero battery failure, and reduced RF beam propagation in thin air. Build an AI-assisted high-altitude anti-drone optimization and thermal-RF simulation platform that dynamically tunes RF jamming frequencies, manages thermal Peltier cooling, and maximizes jammer neutralization range.
Adaptive Variable Resolution 2.5D Lidar Mapping for Dynamic Environment Perception
Autonomous military ground vehicles (UGVs) operating in dynamic tactical combat zones encounter high-speed incoming threats and complex rugged terrain, where fixed-resolution 3D LiDAR creates processing bottlenecks on irrelevant empty space while missing distant hazards. Build an Adaptive Variable-Resolution 2.5D/3D LiDAR Mapping and semantic obstacle segmentation engine that dynamically concentrates laser point density on regions of interest (ROI), moving threats, and traversability bottlenecks.
Smart Scan strategy for Electronic Warfare
In modern contested electromagnetic battlefields, enemy radars constantly change pulse repetition frequencies (PRF), beam scanning patterns, and frequency-hopping sequences to evade detection, while conventional wideband Electronic Warfare (EW) receivers suffer from blind spots and high search latency. Build an AI-driven Smart Scan Strategy and cognitive radar interception platform for DRDO that uses Reinforcement Learning and Bayesian search to dynamically optimize receiver dwell times, anticipate agile radar sweeps, and maximize probability of intercept (POI).
AI-Powered Automated Underwater Marine Debris and Anomaly Detection System using Side-Scan Sonar Imagery
Marine plastic debris, discarded ghost fishing nets, and submerged pollutants in coastal and deep Indian waters degrade marine biodiversity and threaten submarine infrastructure with virtually zero automated underwater detection systems. Build an AI-powered automated underwater marine debris and anomaly detection platform for Autonomous Underwater Vehicles (AUVs) and ROVs that uses deep learning computer vision with underwater optical restoration (water turbidity dehazing) to identify, classify, and map marine litter on the seafloor.
Development of a Low-Power, Real-Time Adaptive Software-Defined Sonar Transmitter Payload for Autonomous Underwater Vehicles (AUVs)
Autonomous Underwater Vehicles (AUVs) exploring deep Indian waters require acoustic sonar for navigation and seafloor mapping, but commercial hardware sonars are bulky, consume excessive battery power, and have rigid unmodifiable signal processing. Build a low-power, real-time Adaptive Software-Defined Sonar (SDS) platform for AUVs in GNU Radio / C++ that implements reconfigurable acoustic waveforms (LFM chirp, CW pulses), adaptive matched filtering, and side-scan bathymetric image reconstruction.
AI-Enabled Antarctic Sea-Ice, Iceberg Trajectory, and Navigation Decision Support System
Indian Antarctic expeditions (vessels travelling from Cape Town to Bharati/Maitri stations) face catastrophic iceberg collisions, thick pack-ice besetting, and severe polar storms with delayed satellite ice charts. Build an AI-enabled Antarctic sea-ice and iceberg trajectory forecasting decision support platform combining multi-mission SAR satellite imagery (Sentinel-1 / RISAT), optical MODIS feeds, and ocean-wind drift physics to calculate optimal fuel-efficient and safe polar navigation corridors.
OceanEmbed - Satellite Embedding-Based Deep Learning Framework for Reconstruction of Subsurface Ocean Temperature from Surface Satellite Observations.
Raw satellite ocean observations (SST, Chlorophyll-a, Sea Surface Height) have missing data gaps from cloud cover and low spatial resolution, limiting marine fisheries and cyclone track models. Build 'OceanEmbed'—a self-supervised foundation model that generates dense spatio-temporal embeddings from multi-satellite rasters to reconstruct missing oceanic fields, super-resolve Sea Surface Temperature (SST) to 1 km resolution, and predict Potential Fishing Zones (PFZ) for INCOIS.
Develop a web-based interactive 3D visualization platform that integrates numerical ocean model outputs and in-situ observations.
INCOIS and oceanographic scientists generate complex multi-dimensional ocean data (3D temperature depth profiles, salinity thermoclines, subsurface currents, bathymetric trenches) that are locked in static 2D plots, preventing intuitive visual analysis. Build an interactive web-based 3D oceanographic visualization and digital twin platform using WebGL / CesiumJS that renders multi-depth oceanic layers, animated particle current vectors, and interactive vertical thermocline slices.
WeatherGPT: Conversational AI for Weather Forecasting, Alerts, and Climate Information
Citizens and farmers in India struggle to understand complex technical weather bulletins (convective available potential energy, isobars, hectopascals) issued by the India Meteorological Department (IMD), while static mobile apps fail to answer specific local questions. Build 'WeatherGPT'—a conversational AI weather intelligence platform combining grounded IMD/NCMRWF forecasts, live Doppler radar feeds, and regional language voice LLMs to provide hyper-localized weather advice, agricultural spray advisories, and disaster safety alerts.
To develop an Artificial Intelligence (AI) / Machine Learning (ML) based system for identification, classification, and prediction of different tropical cyclone patterns using multi-source satellite data.
Dense winter radiation fog across the Indo-Gangetic Plains (Delhi, Punjab, UP, Bihar) disrupts hundreds of flights, passenger trains, and highway freight operations daily with low-visibility conditions (<50 meters) that existing numerical models fail to forecast accurately at sub-kilometer scales. Build an AI/ML-based high-resolution fog and visibility nowcasting system combining satellite land surface temperature, boundary layer humidity sensors, and Ceilometer backscatter to forecast Runway Visual Range (RVR) up to 12 hours in advance.
AI/ML-Based Integrated heavy rainfall Early Warning and Inundation Prediction System using Satellite, Radar, observational Weather and numerical weather prediction model data.
Extreme monsoon downpours in major Indian cities (Mumbai, Chennai, Bengaluru, Delhi) cause catastrophic urban flooding within 60 minutes due to outdated stormwater drain capacities and lack of integrated hydraulic modeling. Build an AI-driven integrated heavy rainfall early warning and 2D urban inundation prediction system coupling Doppler weather radar rainfall nowcasts with municipal drainage networks to forecast street-level water logging depth.
AIML based Nowcasting of thunderstorm and lightning using atmospheric observation including multiple radars, satellite, lightning and model data.
Severe thunderstorms, squalls, and cloud-to-ground lightning strikes kill over 2,500 rural farmers and laborers annually in India due to sudden onset and lack of localized nowcasting. Build an AI/ML-based nowcasting and early warning platform for IITM/IMD that ingests Lightning Location Network (LLN) pulses, Doppler radar reflectivity, and INSAT-3D cloud-top temperatures to forecast thunderstorm cell paths and lightning strike hazard zones 30-90 minutes in advance.
AI/ML-Based Intelligent Anomaly Detection for Automatic Weather Stations (AWS)
The India Meteorological Department (IMD) operates a nationwide network of 1,200+ Automatic Weather Stations (AWS), but field sensors suffer from sensor drift, clogged rain gauge funnels, solar battery depletion, and bird roosting, leading to erroneous data entering national numerical weather prediction models. Build an AI/ML-based intelligent anomaly detection and automated quality control platform for IMD that flags stuck sensors, physical drift, and temporal-spatial inconsistencies in real time.
Participants are invited to design and develop **CAPACITY CONNECT A Digital Capacity Building and Learning Management Portal** to support organizational training, competency development, and knowledge sharing through a centralized web-based platform.
Indian researchers, universities, and students struggle to access advanced scientific infrastructure (oceanographic research vessels, supercomputing clusters, polar test facilities, mass spectrometers) across MoES institutes (NCPOR, NIOT, INCOIS, IITM, NCMRWF) due to lack of a unified booking and training ecosystem. Build 'CAPACITY CONNECT'—a centralized digital research capacity building, scientific equipment sharing, and internship management platform for the Ministry of Earth Sciences (MoES).
AI-Driven Hyper-Local Early Warning System for Severe Weather Nowcasting
Severe local convective storms (thunderstorms, squalls, microbursts) develop and dissipate within 1 to 2 hours over small 5-10 km areas, evading traditional numerical models. Build an AI-driven hyper-local severe weather nowcasting system combining dual-polarization Doppler weather radar volumetric scans, INSAT-3D rapid-scan infrared imagery, and surface AWS networks to generate automated 0-2 hour convective storm cell tracks and wind gust nowcasts at 500m resolution.
AI-Driven Spatio-Temporal Tracking of Extreme Weather Anomalies in Medium-Range Forecasts
National Centre for Medium Range Weather Forecasting (NCMRWF) runs global and regional Numerical Weather Prediction (NWP) ensemble models (NCUM) producing massive multi-dimensional forecast grids, where detecting emerging extreme weather anomalies (monsoon low-pressure depressions, heat dome patterns, atmospheric rivers) across 10-day forecast horizons requires tedious manual analysis. Build an AI-driven Spatio-Temporal Extreme Weather Anomaly Tracker for NCMRWF that uses 3D Convolutional Neural Networks and extreme value statistics to automatically identify, track, and rank extreme meteorological anomalies across ensemble forecast members.
Extreme Heatwave Early Warning and Human Thermal Stress Index
Severe summer heatwaves across India claim thousands of lives and cause massive labor productivity loss, while standard temperature forecasts fail to capture the lethal physiological threat of 'Wet-Bulb Temperature' (combined extreme heat and high humidity where the human body cannot cool down through sweating). Build an AI-driven Extreme Heatwave Early Warning and Human Thermal Stress Index platform for IMD that computes localized Wet-Bulb Globe Temperature (WBGT), Universal Thermal Climate Index (UTCI), and neighborhood-level heat vulnerability heatmaps.
Convective scale nowcasting for Thunderstorms, Hail & Cloudbursts (06 hr)
Severe localized convective cloudbursts, severe hailstorms, and squall lines across the Himalayas and central plains develop within 30 minutes, causing sudden flash floods and severe crop destruction before traditional numerical models can react. Build an AI-driven convective scale nowcasting platform for IMD (0-6 hour forecast horizon) that assimilates 3D Doppler radar reflectivity grids, INSAT-3D rapid-scan cloud-top cooling rates, and GNSS Integrated Water Vapor (IWV) to predict cloudburst initiation and hail size with sub-kilometer precision.
Urban Flood Nowcasting System (Drainage and Rainfall Coupling)
Indian metropolitan cities (Bengaluru, Chennai, Mumbai, Hyderabad) suffer chronic urban waterlogging where water accumulates in specific street intersections within 30 minutes of rain, but municipal corporations lack real-time visibility into drainage bottleneck points. Build an Urban Flood Nowcasting and Drainage Coupling System for IMD and Municipal Corporations that couples high-resolution Doppler radar rainfall nowcasts with municipal stormwater pipe GIS topologies to predict sub-catchment waterlogging depths and pump dispatch schedules.
Hyperlocal Monsoon Onset & Break Prediction System (Block/Village Scale)
Indian farmers make critical crop sowing and seed investment decisions based on the onset and dry-spell break periods of the Southwest Monsoon, but current IMD onset forecasts are issued at broad regional scales (e.g. 'Monsoon onset over Kerala'), failing to predict the exact village-level arrival date or prolonged 2-3 week agricultural break phases. Build an AI-driven Hyperlocal Monsoon Onset & Break Prediction System for IMD that predicts village-scale onset dates, dry-spell break durations, and sowing risk windows 15-30 days in advance.
Cooperative Gig Services Platform for Household & Community Services
Rural and semi-urban gig workers (electricians, plumbers, carpenters, domestic workers, care providers) in India are exploited by private gig platforms charging 20-30% commissions without social security or collective bargaining power. Build a Worker-Owned Cooperative Gig Services Platform for the Ministry of Cooperation that enables local worker cooperatives to manage household and community service bookings, retain 100% of service earnings, access collective health insurance, and manage democratic cooperative voting.
AI-Driven Hyper-Local Business Advisory and Financial Structuring Assistant for Rural Micro-Entrepreneurs
Marginalized entrepreneurs from SC/ST and backward communities supported by NBCFDC/NSKFDC struggle to draft bankable Detailed Project Reports (DPR), calculate unit economics, and secure MSME loans due to lack of financial literacy and costly private consultants. Build an AI-driven Hyper-Local Business Advisory and Financial Structuring Assistant that uses conversational vernacular voice guidance to generate P&L projections, debt-service coverage ratios (DSCR), and 1-click Mudra/Stand-Up India loan applications.
AI-Driven Scheme Matching for Marginalized Entrepreneurs
Marginalized entrepreneurs (SC/ST, OBC, Safai Karamcharis, Persons with Disabilities) miss out on hundreds of central and state welfare schemes (Stand-Up India, Mudra, PM-VISHWAKARMA, NBCFDC term loans, capital subsidies) due to fragmented portals and confusing eligibility rules. Build an AI-driven Scheme Matching and 1-Click Application platform for the Ministry of Social Justice that matches enterprise profiles against 500+ government welfare schemes with automated eligibility scoring and pre-filled application packages.
Digital Heritage Archive for Memorials, Manuscripts & Ambedkar: AI-Powered Institutional Archive and Audio-Visual Knowledge Platform
Historical manuscripts, speeches, legal writings, and rare photographs of Dr. B.R. Ambedkar and national social reform memorials are scattered across physical archives, vulnerable to paper degradation and inaccessible to global scholars. Build an AI-Powered Digital Heritage Archive for Dr. Ambedkar Memorials and Social Justice Literature featuring high-resolution multilingual manuscript OCR, semantic knowledge graph search across 21+ volumes of Writings and Speeches, and 3D virtual memorial tours.
AI-Driven voice Assistant for livelihood Mapping and NSQF-Aligned Skilling Recommendations for SC Communities under GIA component of PM-AJAY
Youth from marginalized communities (SC/ST, sanitation worker families, rural artisans) lack career guidance and struggle to navigate complex National Skills Qualifications Framework (NSQF) vocational courses. Build an AI-driven Vernacular Voice Assistant for Livelihood Mapping and NSQF-Aligned Skill Recommendations that assesses an individual's existing informal skills through interactive voice conversations, maps local job market demand, and enrolls them in certified government skilling programs.
AI-Driven Standardization and Harmonization of Material Codes Across CPSEs
Major Central Public Sector Enterprises (CPSEs) under the Ministry of Petroleum & Natural Gas (ONGC, IOCL, BPCL, HPCL, GAIL, MRPL) maintain millions of spare part inventories with conflicting legacy material codes and inconsistent naming, making cross-CPSE procurement sharing and aggregate buying impossible. Build an AI-driven Material Code Standardization and Harmonization Platform that uses NLP and entity resolution to deduplicate spare parts, standardize technical descriptions, and map internal codes to international standards (UNSPSC, MESC, NATO Codification).
Develop an AI enabled learning platform that identifies competency gaps, recommends personalized training through integration with the iGOT Karmayogi ecosystem, and capable of generating Quizzes and Multiple choice questions (MCQs) from uploaded learning materials to strengthen capacity building in India's Official Statistical System.
Statistical officers and data investigators at the Ministry of Statistics and Programme Implementation (MoSPI) require continuous upskilling in advanced econometrics, big data analytics, sampling design, and Python/R data science, but existing training is static and classroom-bound. Build an AI-enabled Adaptive Learning and Competency Gap Platform for MoSPI that assesses individual statistical competency levels, identifies skill deficiencies across job roles, and delivers personalized interactive micro-learning modules with coding sandboxes.
AI-Powered Continuous Cyber Risk Quantification and Investment Optimization Platform
Higher education institutions and universities under AICTE face escalating ransomware attacks and data breaches, but CISOs struggle to quantify cyber risk in monetary terms (INR) to justify cybersecurity budgets to university boards. Build an AI-powered Continuous Cyber Risk Quantification (CRQ) and Security Investment Optimization Platform for AICTE institutes based on the FAIR (Factor Analysis of Information Risk) framework that continuously maps vulnerability scans, calculates Probable Maximum Loss (PML), and recommends optimal ROI cybersecurity investments.
AI-Powered Email Threat Detection, GeoLocation and Forensic Intelligence Platform
Higher educational institutions and government agencies face sophisticated Business Email Compromise (BEC), spear-phishing, and domain spoofing attacks that bypass conventional keyword spam filters. Build an AI-Powered Email Threat Detection, Geolocation, and Forensic Intelligence Platform for AICTE that parses raw RFC 822 email headers, analyzes DKIM/SPF/DMARC cryptographic alignments, traces intermediate SMTP relay hops on a global threat map, and uses NLP to detect CEO impersonation and urgent financial wire fraud cues.
Al-Based Predictive Modelling for Early Forecasting of Bovine Mastitis in lndian Dairy Farms
Bovine Mastitis (bacterial udder inflammation) causes over ₹13,000 Crores in annual milk yield losses to Indian dairy farmers, where sub-clinical mastitis goes undetected by visual inspection until irreversible udder damage occurs. Build an AI-based predictive modeling and early forecasting platform for the Department of Animal Husbandry & Dairying (DAHD) that analyzes automated milking conductivity telemetry, milk thermal imaging, somatic cell count (SCC) proxies, and animal wearable activity to forecast mastitis 48 hours before clinical symptoms appear.
eRTMAC-NWIS (Nearby Wells Intelligence System): An AI-Powered Offset Well Knowledge and Decision Support Platform for Drilling Operations
Drilling engineers at Oil India Limited (OIL) encounter unexpected drilling hazards (formation kick, lost circulation, stuck pipe, shale swelling) because offset well data (drilling logs, mud weights, lithology, casing seats) is locked in legacy well archives and scattered daily drilling reports (DDRs). Build 'eRTMAC-NWIS' (Nearby Wells Intelligence System)—an AI-powered offset well correlation and real-time drilling risk forecasting platform for OIL that spatializes historical borehole logs and predicts formation pore pressures to prevent blowouts.
AI-Powered Mobile Urban Intelligence Platform Using Public Transport Fleet
Smart city surveillance and road infrastructure monitoring across Indian municipal corporations is hindered by the high cost of installing fixed road cameras every 100 meters. Build an AI-Powered Mobile Urban Intelligence Platform for Bharat Electronics Limited (BEL) that mounts edge-AI camera units on public city buses to continuously detect road potholes, illegal roadside encroachment, garbage overflow, and missing streetlights during daily transit routes.
City-Wide AI Engine for Multi-Camera ANPR Trajectory Tracking and Urban Traffic Analytics
Police and traffic authorities across major Indian cities struggle to track stolen suspect vehicles or reckless drivers across thousands of disconnected municipal CCTV cameras because manual camera switching is slow and vehicles change appearance across different lighting angles. Build a City-Wide AI Engine for Multi-Camera ANPR Trajectory Tracking and Urban Mobility for Bharat Electronics Limited (BEL) that automatically extracts vehicle license plates, computes spatial-temporal re-identification (ReID) embeddings, and reconstructs 2D/3D vehicle travel routes on a city GIS map in real time.
Efficient systems for early detection,prevention,and management of livestock diseases and animal health issues
Rural livestock farmers in Maharashtra suffer massive economic losses from infectious cattle and goat disease outbreaks (Lumpy Skin Disease, Foot-and-Mouth Disease, Peste des Petits Ruminants) that spread rapidly between villages before district veterinary officers are alerted. Build an AI-driven Livestock Epidemic Early Warning, Tele-Veterinary Diagnosis, and Outbreak Surveillance Platform for the Government of Maharashtra that uses smartphone computer vision to detect early skin lesions and spatio-temporal clustering to contain disease outbreaks.
Early detection and management of crop diseases and pest infestations
Farmers in Maharashtra (Vidarbha, Marathwada, Western Maharashtra) suffer catastrophic crop failures from devastating pest infestations (Pink Bollworm in cotton, Fall Armyworm in maize, Soybean stem fly) because conventional extension services identify pests too late after widespread crop damage. Build an AI-driven Mobile Crop Pest & Disease Diagnostic and Epidemiological Early Warning Platform for the Government of Maharashtra (MahaAgri) that diagnoses crop diseases from smartphone leaf photos in offline fields, provides localized organic/chemical treatment advisories in Marathi, and maps taluka-level pest spread.
Deep Learning Based Super Resolution Mapping (SRM) from Medium Resolution Satellite Imageries
National security and geospatial surveillance agencies under the National Technical Research Organisation (NTRO) rely on medium-resolution satellite imagery (10 to 30 meters from Sentinel-2, Landsat) which is widely available but too blurry to resolve strategic infrastructure (airfield runways, coastal naval docks, military vehicle convoys). Build a Deep Learning-Based Super Resolution Mapping (SRM) and Spatial Feature Restoration Platform for NTRO that uses generative adversarial super-resolution networks (ESRGAN / Diffusion models) to upscale 10m satellite imagery to <2.5m high-resolution sub-pixel intelligence.
Leveraging satellite imagery to determine Oil spills at sea along with AIS data correlations to identify vessel responsible for the spill.
Illegal maritime oil spills, tank-wash discharges, and offshore drilling accidents in Indian Exclusive Economic Zones (EEZ) cause severe marine ecological damage, while delayed satellite detection allows polluter vessels to flee without liability. Build an AI-Powered Satellite Oil Spill Detection and Trajectory Drift Forecasting Platform for the National Technical Research Organisation (NTRO) combining Sentinel-1 SAR radar imagery, optical MSI feeds, AIS vessel tracking, and ocean hydrodynamic drift modeling to detect oil slicks and identify the culprit ship.
Dark web threat actor de-anonymization
Hostile threat actors, ransomware cartels, and cyber espionage groups operate behind Tor onion routing, PGP encryption, and cryptocurrency mixers on the dark web, leaving intelligence analysts with fragmented alias trails. Build a Dark Web Threat Actor De-Anonymization and Intelligence Correlation Platform for NTRO that crawls hidden onion services, analyzes PGP key cross-references, extracts digital styling fingerprints (stylometry), and maps cryptocurrency transactions to de-mask adversary identities across surface and dark web platforms.
Social Media Analytics
Foreign psychological operations, coordinated disinformation campaigns, and radicalization networks weaponize social media platforms (X/Twitter, Telegram, YouTube, Reddit) during national security crises to incite communal riots and stock market panic. Build an AI-Powered Social Media Intelligence, Coordinated Inauthentic Behavior (CIB) Detection, and Threat Analytics Platform for NTRO that tracks real-time cross-platform narratives, unmasks synchronized botnet swarms, and maps sentiment polarization.
AI based Network Attack Forecasting from Network Traffic Data
Critical government and defense IP networks face Advanced Persistent Threats (APTs), zero-day exploits, and multi-stage cyber attacks where existing Intrusion Detection Systems (IDS) only generate reactive alerts after a breach has occurred. Build an AI-Based Network Attack Forecasting and Proactive Threat Trajectory Prediction Platform for NTRO that analyzes passive NetFlow/IPFIX telemetry, models multi-stage cyber attack kill chains (MITRE ATT&CK), and forecasts the adversary's next target host 30-60 minutes before lateral movement occurs.
AI-Driven Multi-Vendor Network Security Compliance Auditor
Enterprise and government network backbones under NTRO operate thousands of heterogeneous routers, switches, firewalls, and VPN gateways from multiple vendors (Cisco, Juniper, Fortinet, Check Point, Palo Alto, Huawei) with thousands of lines of conflicting CLI configurations, causing security vulnerabilities and regulatory non-compliance with national cyber security guidelines (CERT-In, CIS Benchmarks, NIST). Build an AI-Driven Multi-Vendor Network Security Compliance Auditor for NTRO that automatically ingests heterogeneous raw device configurations, builds a unified abstract network model, audits security rules against CIS/CERT-In benchmarks, and generates remediation scripts.
Single-Pass Drone Video to Accurate 3D Model Generation System
Tactical reconnaissance drones deployed by NTRO capture single-pass monocular video over target areas, but classical photogrammetry tools (like Pix4D/Metashape) require hours of multi-pass flight overlap and heavy offline compute to generate 3D elevation models. Build a Single-Pass Drone Video to 3D Photorealistic Mesh Reconstruction Platform for NTRO that uses monocular depth estimation, Neural Radiance Fields (NeRF) / 3D Gaussian Splatting, and structure-from-motion to generate georeferenced 3D digital twins in minutes.
Dam Break Inundation Modelling Using Hydrodynamic Modelling of any River
Catastrophic dam failures in India (due to cloudbursts, earthquakes, structural breach) cause devastating downstream flash floods and loss of life within hours, where disaster authorities lack real-time hydrodynamic breach simulation models to plan evacuations. Build an AI-Accelerated Dam Break Inundation and River Hydrodynamic Modeling Platform for NTRO combining dam breach breach-parameter physics (Froehlich / MacDonald formulas), 2D shallow water hydrodynamic solvers (HEC-RAS / LISFLOOD), and high-resolution DEMs to forecast downstream flood arrival times and evacuation corridors in real time.
AI-Based Detection and Classification of Industrial Fires and Persistent Thermal Sources Using NASA FIRMS, OSM & Satellite Data
Industrial fires, illegal chemical factory blazes, gas pipeline blowouts, and coal seam fires across India produce persistent thermal hotspots that cause toxic air pollution and industrial disasters, while standard satellite fire monitors (like FIRMS) suffer from 12-hour orbit delays and low spatial resolution. Build an AI-Based Industrial Fire and Persistent Thermal Hotspot Detection Platform for NTRO combining geostationary rapid-scan satellites (INSAT-3D/3DR), high-resolution Sentinel-2 / Landsat short-wave infrared (SWIR), and deep learning to detect and classify industrial fires within 15 minutes of ignition.
Multi-modal, Sun angle and scale invariant image correspondence using Chandrayaan-2 optical images (OHRC, TMC and IIRS)
Automated co-registration of satellite images captured at different seasons, sun illumination angles, sensor modalities (Optical vs SAR Radar), and spatial resolutions (Cartosat vs Sentinel) suffers from high geometric distortion, parallax errors, and feature matching failures over rugged Indian terrain. Build an AI-Driven Multi-Modal, Sun-Angle, and Scale-Invariant Image Correspondence and Geo-Registration Engine for ISRO that uses deep local invariant feature descriptors (SuperPoint / LoFTR) to achieve sub-pixel spatial alignment across heterogeneous satellite acquisitions.
SatQuery AI - An Interactive Vision-Language Assistant for Multimodal Remote Sensing Image Analysis through Text Queries
Scientists, urban planners, and disaster responders struggle to query massive archives of Indian satellite imagery on ISRO's Bhuvan and Bhoonidhi portals because finding specific geospatial insights (e.g. 'Show flood inundation in Kaziranga between June and August 2024' or 'Count solar farms in Rajasthan') requires specialized GIS software and manual band-math processing. Build 'SatQuery AI'—an Interactive Vision-Language Assistant and Spatial Grounding Engine for Multimodal Earth Observation for ISRO that understands natural language queries, performs zero-shot satellite object detection, and generates analytical GIS summary reports.
DepthWizard - Single-View Height Estimation and 3D Flythrough
Disaster responders, defense intelligence officers, and urban planners working with ISRO satellite data frequently have access to only single optical monocular satellite images (with no stereo pairs or LiDAR elevation data available), making 3D elevation analysis and building height measurements impossible. Build 'DepthWizard'—a Single-View Height Estimation and 3D Photorealistic Flythrough Generation Platform for ISRO that uses deep monocular depth foundation models, solar shadow geometry trigonometry, and 3D neural rendering to reconstruct 3D cityscapes from a single 2D satellite image.
ORCA Marine EcOsystem Reasoning with Collaborative Agents
Marine ecosystem conservation and fisheries management across the Indian Ocean require complex multi-disciplinary reasoning across sea surface temperatures, chlorophyll ocean blooms, coral bleaching, illegal fishing vessel tracks, and climatic El Niño oscillations that no single AI model can analyze holistically. Build 'ORCA' (Marine Ecosystem Reasoning with Collaborative Agents) for ISRO—a multi-agent vision-language reasoning platform that coordinates specialized AI agents (Oceanographer Agent, Fisheries Agent, Coral Health Agent, Vessel Surveillance Agent) to solve complex marine conservation inquiries.
A deployable AI-powered autonomous drone that aids search-and-rescue operations by detecting people and hazards, thereby improving responder safety and reducing victim discovery time.
Disaster search-and-rescue operations during earthquakes, landslides, and collapsed building incidents in India suffer from slow human exploration in hazardous rubble and GPS-denied indoor environments where cellular networks are destroyed. Build an Autonomous Edge-AI Search-and-Rescue Drone powered by the Qualcomm Snapdragon Flight / RB5 platform that uses on-device computer vision, thermal infrared human detection, visual SLAM, and acoustic survivor cry localization to locate trapped victims in real time.
A resilient, AI-powered environmental monitoring network that provides early detection, localized intelligence, and actionable alerts for floods, forest fires, pollution events, and other environmental hazards common in India, enabling authorities and communities to shift from reactive disaster response to proactive risk prevention.
Industrial complexes, national parks, and smart cities in India require continuous environmental monitoring (toxic gas leaks, wildfire smoke, air particulate matter, water runoff contamination), but existing sensor networks suffer from cellular single points of failure, solar battery depletion, and high maintenance costs. Build a Resilient, AI-Powered Autonomous Environmental Monitoring Mesh Network for Qualcomm Inc using Qualcomm RB5 / Snapdragon IoT edge gateways that orchestrates low-power LoRa/Zigbee sensor nodes, on-device anomaly detection, and self-healing mesh routing.
Automated Attribution of Unknown Cryptocurrency Wallets to Nearest Virtual Asset Service Providers (VASPs) through Blockchain Intelligence APIs
Indian cybercrime police investigating multi-crore investment scams, ransomware, and digital arrest frauds track stolen money into anonymous cryptocurrency wallets, but law enforcement cannot issue statutory Section 91 CrPC notices because identifying which Virtual Asset Service Provider (VASP / crypto exchange) controls the target wallet takes weeks of manual forensic analysis. Build an Automated Attribution and Clustering Platform for Unknown Cryptocurrency Wallets for the Ministry of Home Affairs (MHA / I4C) that uses Graph Neural Networks and on-chain heuristic clustering to attribute unknown wallet addresses to the nearest regulated VASP exchange in seconds.
Development of a Predictive Analytics Framework for Cybercrime Complaints to Forecast Likely Cash Withdrawal Locations in Advance, Enabling Generation of Actionable Intelligence for Timely and Proactive Cybercrime Intervention.
The National Cybercrime Reporting Portal (1930 helpline / cybercrime.gov.in) receives over 5,000 daily citizen complaints where lack of automated categorization and predictive analytics allows coordinated cross-state cybercrime syndicates (Jamtara phishing, Mewat sextortion, Chinese loan apps, digital arrest scams) to operate undetected across jurisdictional boundaries. Build an AI-Powered Predictive Analytics Framework for Cybercrime Complaints for MHA / I4C that clusters complaints by modus operandi (MO), predicts emerging cybercrime hotspots, and correlates suspect phone numbers, IMEI numbers, and bank accounts across state police forces.
Intelligent Identification of Hazard-Based Red Zones, Carrying Capacity Assessment, and Immediate Relocation Needs for Vulnerable Habitations
Fragile Himalayan eco-sensitive pilgrimage towns (Joshimath, Kedarnath, Manali, Shimla) face catastrophic land subsidence, slope destabilization, and traffic gridlocks due to unregulated tourist influx exceeding local ecological carrying capacities. Build an AI-Driven Hazard-Based Red Zone and Dynamic Tourist Carrying Capacity Management Platform for the Ministry of Home Affairs (MHA / NDMA) that fuses InSAR satellite ground deformation, geotechnical slope stability models, and real-time mobile cell-tower crowd densities to dynamically regulate tourist entry permits and establish prohibited construction zones.