Background

Accurate and up-to-date urban land records are essential for effective land governance, urban planning, taxation, infrastructure development, and delivery of citizen-centric services. At present, preparation of cadastral maps and delineation of urban parcel boundaries is largely dependent on manual interpretation of drone imagery and field-based Ground Truthing (GT) activities. The process is time- consuming, resource intensive, and requires extensive human intervention for extraction of parcel boundaries, building footprints, road networks, and other cadastral features.Further, dense urban settlements, irregular parcel geometries, encroachments,overlapping structures, narrow access roads, and mixed land-use patterns create significant challenges in preparation of accurate parcel maps. Manual digitization and validation of parcel boundaries often lead to delays in completion of cadastral surveys and generation of urban land records.With availability of high-resolution orthorectified lmagery (ORl), Digital surface Models (DSM), Digital Terrain Models (DTM), and drone datasets, there exists significant potential for leveraging Artificial lntelligence (Al), computer Vision, and GeoAl technologies for automated extraction of cadastral features and preparation of preliminary urban Parcel maps.

Description

The system should be capable of:

Expected Solution

The expected outcome is development of an Al-enabled automated cadastral mapping platform capable of significantly reducing manual efforts involved in urban parcel mapping and cadastral preparation. The final solution should

Background

Land governance in lndia involves multiple institutions maintaining land-related information in fragmented and disconnected systems. Core datasets such as cadastral maps, Record of Rights (RoR), registration records, land use information, Master Plan, Building Permission, Restrictions, property taxation records, utility infrastructure, and other land-related databases are often managed Independently by different departments and agencies with limited interoperability. This results in duplication of effort, inconsistencies in records, delays in obtaining ownership information, lack of transparency in transactions, and inconvenience to citizens seeking land-related services. The growing scale of urbanization, increasing land transactions, demand for efficient governance, and the need for transparent and citizen-centric public service delivery require a modern digital approach to land administration. With advances in GIS technologies, Digital Public lnfrastructure (DPl), cloud computing, interoperable APls, Al/ML analytics, and geospatial standards, there is an opportunity to transform land governance through a unified digital ecosystem.Land Stack is envisaged as an integrated GIS-based digital platform that brings together all land-related datasets, workflows, and services into a single interoperable framework. Built upon georeferenced cadastral maps and linked with record of rights(land ownership records), Land Stack can serve as foundational digital infrastructure for efficient land governance, informed decision making, improved service delivery.The proposed platform should support both rural and urban contexts and enable seamless coordination across departments, institutions, and citizen interfaces.

Detailed Description

The Department of Land Resources has initiated the development and deployment of Land Stack in pilot locations of Chandigarh and Tamil Nadu, launched on 31 December 2025. Following successful implementation, the platform is proposed to be expanded across lndia by covering one city and one village in every State and Union Territory, and subsequently scaled to achieve nationwide coverage.One of the major challenges in lndia is that land is a State subject, resulting in significant diversity in land administration systems across states. Variations exist in land record formats, database structures, units of measurement, number and type of fields, language, terminology, and administrative workflows. Therefore, the challenge is to conceptualize and develop a scalable prototype of Land Stack capable of integrating diverse land-related datasets, workflows, and services into a common interoperable State-level framework. The proposed Land stack solution should organize information into three broad categories of spatial layers. The base layer should comprise georeferenced cadastral maps, parcel boundaries, and unique parcel identifiers such as ULPIN. This foundational layer should provide the spatial framework upon which all governance and service-related datasets can be integrated.The essential layers should include core governance datasets linked to each parcel,such as Record of Rights (RoR), registration data, master plans, building permissions and approvals, encumbrance and mortgage records, land use and zoning information.These layers should collectively define ownership, rights,restrictions, liabilities, and permissible land use associated with each parcel.Beyond this, additional or use-case layers should extend the platform's governance and citizen service capabilities by integrating datasets such as utility infrastructure, property taxation records, valuation references, infrastructure networks,environmental or restriction zones, and other service linkages. Each land parcel should be uniquely identifiable and linked with multiple layers of governance and administrative information, with ULPIN serving as the suggested common identifier.The prototype should demonstrate integration of multiple land-related domains through parcel-level GlS visualization and data exploration tools. The system should support interoperability between departmental systems through open APls, standardized metadata structures, secure authentication mechanisms, role-based access controls, audit trails, and scalable digital architecture. Citizen-facing capabilities such as parcel search, ownership verification, transaction status tracking, service requests, and access to land-related information should also be incorporated. Participants are encouraged to integrate innovative technologies including Artificial lntelligence (Al), Machine Learning (ML) , satellite imagery-based change detection, predictive analytics, workflow automation, and decision-support dashboards to improve transparency, operational efficiency, and governance outcomes.The overall solution should be modular, scalable, configurable for different administrative contexts, and capable of serving as a replicable national framework for integrated digital land governance.

Expected Solution

The expected outcome is a functional prototype demonstrating the concept of Land stack as an integrated Gls-based Digital Public lnfrastructure for land governance.The solution should provide a unified digital platform capable of integrating multiple land-related datasets around a parcel-centric spatial framework and enabling seamless interaction between governance institutions, land administration agencles, and citizens. The prototype should demonstrate GIS-based parcel visualization,integration of mock or sample land-related datasets, role-based administrative dashboards, citizen-facing service interfaces, and interoperable workflows between land records, registration, dispute, planning, and fiscal systems. The proposed solution should showcase efficient parcel-level information access, real-time or simulated workflow integration, cross-departmental data interoperability, analytics-driven governance insights, and transparent citizen service delivery mechanisms.lnnovative solutions that leverage Al/ML, geospatial intelligence, predictive analytics,workflow automation, API-based integration, mobile accessibility, and secure cloud- native architecture will be preferred. The final prototype should demonstrate how fragmented land governance systems can be transformed into a unified, scalable, transparent, and citizen-centric Land Stack platform capable of improving land administration, enabling citizens to take informed decisions, accelerating transactions, strengthening planning, and enabling data-driven governance. Further, participants are expected to prepare a Standard Technical Document containing details of API standards, interoperability standards, data schemas, system architecture, GIS standards, security frameworks, UI/UX guidelines, color schemas, and deployment and scalability considerations.

Background

Most autonomous driving systems are developed for roads with clear lane markings, standard signage, predictable traffic flow, and controlled intersections. Indian roads are often very different. Vehicles of many types share the same space, including cars, buses, trucks, auto-rickshaws, twowheelers, bicycles, pedestrians, pushcarts, and animals. Drivers and pedestrians may change direction suddenly, merge without signalling, drive against traffic, or cross at unmarked locations. In many areas, road edges are unclear, potholes are common, and formal lane discipline is limited. These conditions make it difficult for traditional path planning methods that depend on structured road geometry and predictable motion. India has a large and diverse road network that includes village roads, crowded market areas, urban intersections, and highways. To support the safe deployment of autonomous vehicles in such environments, students must build planning systems that can adapt in real time to uncertainty, mixed traffic, and changing road conditions.

Description

Design and simulate an adaptive path planning system for an autonomous vehicle that operates in unstructured Indian road conditions. The system should perceive the environment using a multi-sensor setup such as camera, LiDAR, and radar, and identify diverse road users and obstacles, including auto-rickshaws, pushcarts, pedestrians, and animals. It should predict the short-term motion of surrounding agents, including non-lane-based and irregular movement patterns, and generate a safe, collision-free path that can be replanned in real time. The solution should also handle practical driving situations such as missing lane markings, informal merging, sudden pedestrian movement, and unexpected obstacles on the road. Teams should validate their solution using at least five realistic Indian road scenarios, such as an unmarked village road, a busy urban intersection without signals, a highway merge involving slow-moving vehicles, a dense market area with mixed traffic, and a sudden cattle-crossing event. Teams are encouraged to use MathWorks tools such as RoadRunner for scenario design, Automated Driving Toolbox for sensor modeling and fusion, Navigation Toolbox and Stateflow for planning and decision logic, Vehicle Dynamics Blockset or a Simulink bicycle model for vehicle behavior, and Deep Learning Toolbox for detection and trajectory prediction.

Expected Solution

The expected solution should include three main parts.First, teams should build a working simulation pipeline that integrates perception, prediction, path planning, decision logic, and vehicle motion in MATLAB and Simulink. Second, teams should create realistic driving scenarios that represent Indian road conditions, including at least two detailed RoadRunner scenes such as a village road and an urban intersection, and use them to test the vehicle across all five required scenarios. Third, teams should present results that show safe and reliable navigation, including collision-free performance, smooth path generation, and timely replanning during changing road conditions. The final submission should include the simulation model, the designed scenarios, performance results with metrics such as replanning latency, path smoothness, and scenario completion rate, a short technical report that explains the approach and design choices, and a demonstration video that shows the vehicle navigating the test scenarios. The solution should demonstrate closed-loop validation of autonomous driving behavior under realistic mixed-traffic conditions.

Background

Medium Altitude Long Endurance (MALE) UAV are increasingly being deployed for Long-duration intelligence, surveillance, reconnaissance (ISR). Communication relay maritime surveillance and strategic defence missions Reliability and availability of propulsion systems are critical for mission success because piston-engine failures during flight may lead to mission abort, asset loss, or unsafe recovery conditions. Conventional engine monitoring systems used in UAVs are primarily thresholdbased and reactive in nature. These systems generally indicate failures only after abnormality has already occurred. Present approaches also have limited capability to estimate remaining useful life (RUL) predict degradation trends, or simulate mission-wise engine behavior under varying environmental and operating conditions. A Digital Twin (DT) framework for aero piston engines can significantly improve predictive maintenance, operational reliability, mission planning, and life cycle management by creating a continuously synchronized virtual representation of the physical engine using real-time sensor data physics-based models and AI/ML techniques. The proposed problem aims to develop an indigenous Digital Twin framework suitable for deployment in MALE UAV ground control and health monitoring architecture. The solution should support real-time engine state estimation, anomaly detection degradation tracking, faultprediction, and mission replay capability.

Description

Develop a scalable and modular digital Twin System for an aero piston engine used in MALE UAV applications. The system shall create a real-time virtual representation of the engine by integrating. Engine sensor data Thermodynamic behavior models Engine performance maps Failure/degradation logit AI/ML based predictive analytics The proposed system should be capable of: Real-time engine parameter visualization Monitoring of engine health indicators Defection of abnormal operating conditions Predicting probable failures before occurrence Estimating degradation trends and Remaining Useful Life (RUL) Simulating engine behavior under different mission profiles and environmental conditions Supporting post-flight analysis and mission replay The system may utilize CAN bus/Socket CAN-based engine data acquisition ECU/FADEC communication interfaces Edge computing architecture Cloud or local server-based analytics AI/ML algorithms far anomaly detection Physics informed modelling approaches Dash board/HMI for operators and maintenance engineers

Expected Solution

The digital twin core framework shall act as the central intelligence layer that continuously mirrors the real aero-piston engine operating onboard the MALE UAV. The framework should establish a dynamic and continuously synchronized virtual representation of the engine using live telemetry, physicsbased models, operational history and AI-Driven analytics. The framework should be designed considering future deployment in defence grade Ground Control Station (GCS), engine test rigs, and fleet-level health monitoring infrastructures. The expected solution should include A. Digital Twin Core Framework Virtual engine model synchronized with live engine data Modular architecture for future scalability Real-time data ingestion capability B. Health Monitoring System The health monitoring system shall continuously assess the condition of engine sub-systems and generate health indices for predictive maintenance. Monitoring of following engine parameter are required RPM Cylinder Head Temperature (CHT) Exhaust Gas Temperature (EGT) Oil Pressure & Temperature Fuel flow Vibration signatures Battery Alternator health Injection timing parameters C. Fault Detection & Predictive Analytics: The system should transition from conventional threshold-based monitoring to intelligent predictive diagnostics. The detection/prediction of following parameters are required: Misfire conditions Injector abnormalities Coding degradation Lubrication issues Sensor drift/ failure Combustion instability Overheating trends Abnormal vibration patterns D. AE/ML Layer: The AI/ML layer shall provide adaptive learning capability for predictive diagnostic and intelligent maintenance planning. Following parameters are required to be captured Anomaly detection algorithms Remaining Useful Life (RUL) estimation Trend analysis Predictive maintenance recommendations E. Simulation & Replay Capability: The system should include simulation tools to reproduce engine behavior and analyses mission scenarios. Following parameters are required to be captured Replay of historical mission data Environmental condition simulation Engine behavior simulation during High Altitude Endurance mission Hot-weather operation Rapid throttle transitions F. Visualization Dashboard The dashboard shall provide an intuitive operational interface for UAV operators, propulsion engineers and maintenance team. A user Interface displaying dashboard should support following: Real-time engine health status Fault alerts Engine efficiency trends Maintenance advisory Mission-wise health reports Deliverables Expected from Teams Functional prototype/software demonstrator Digital twin architecture design Engine Simulation model AI/ML-based anomaly detection module Visualization dashboard Demonstration using simulated or real engine datasets Technical documentation and deployment roadmap Desired Innovation Areas: Participants are encouraged to explore: Physics-informed AI Edge AI for UAV applications Lightweight onboard analytics Hybrid thermodynamic + data-driven models Federated learning approaches Explainable AI for fault diagnosis Secure telemetry architecture Autonomous maintenance advisory systems Technical Expectations from Participants: Teams are expected to demonstrate understanding of: IC engine fundamentals UAV propulsion systems Sensor fusion Embedded systems CAN communication AI/ML analytics Data visualization Simulation modelling Reliability engineering

Background

Modern smart warehouses rely on fleets of Autonomous Mobile Robots (AMRs) to move goods efficiently. As fleet sizes grow, relying entirely on a centralized cloud server for path planning causes high network latency, Wi-Fi dead-zone vulnerabilities, and single-point-of-failure risks.To ensure continuous operation, modern robotics is shifting toward decentralized, edge-computing solutions where robots can talk to each other directly and make split-second decisions on the fly.

Description

The objective is to design a decentralized coordination and collision-avoidance framework for a multi-robot fleet (at least 3 AMRs) operating in a dynamic warehouse environment. The system must run locally on edge hardware (e.g., Raspberry Pi or Jetson Nano onboard each robot) and handle:

Expected Solution

A multi-robot simulation featuring Decentralized Network Stack A peer-to-peer communication protocol where robots share localization data locally. Multi-Agent Path Planning Implementation of algorithms for edge hardware. Fleet Dashboard A lightweight monitoring UI that visualizes the entire fleet's real-time positions and battery status.

Success Criteria

Zero inter-robot collisions and a minimum 20% reduction in total task completion time compared to traditional stop-and-wait methods when handling overlapping paths.

Background

Outdoor Unmanned Ground Vehicles (UGVs) face unpredictable terrain, changing light, and unreliable GPS signals. To achieve true autonomy in applications like search-and-rescue,agriculture, or delivery, UGVs must rely on onboard computer vision. Visual perception provides a cost-effective, data-rich way for vehicles to understand and safely navigate complex,unstructured outdoor surroundings.

Description

The objective is to build an autonomous navigation system for a UGV operating in a GPS-denied outdoor environment using camera feeds as the primary sensor. Students must solve three key HEADING challenges

Expected Solution

A functional software module consisting of Perception AI A lightweight model for obstacle and path detection. Visual SLAM/Odometry: A pipeline to track vehicle movement. Path Planner An algorithm to translate visual data into wheel/motor commands.

Success Criteria

Successful, collision-free navigation from Point A to Point B across outdoor scenarios

Background

Generation of accurate 3D models of buildings, infrastructure, terrain, and objects typically requires multiple drone passes, extensive image overlap, specialized flight planning, and significant post-processing time. In operational scenarios such as disaster response, surveillance, infrastructure inspection, military reconnaissance, and rapid mapping, there is often only a single opportunity to capture data over the target area. A solution capable of generating an accurate and textured 3D model from a single drone pass video would significantly reduce mission time, operator effort, data acquisition requirements, and processing complexity while enabling near real-time situational awareness.

Description

Design and develop an AI-enabled system capable of generating a georeferenced and metrically accurate 3D model of a scene using only a single-pass drone video stream captured from a moving UAV. The system should process video frames captured during one flight path and reconstruct: (i) 3D terrain and structures (ii) Building facades and rooftops (iii) Roads and infrastructure (iv) Vegetation and obstacles (v) Textured 3D meshes or point clouds Expected Solution/Deliverables: The generated model should be suitable for visualization, measurement, and analysis purposes. Key Challenges (i) Limited viewing angles due to single flight path. (ii) Motion blur and video compression artifacts. (iii) Variable illumination and shadows. (iv) Dynamic objects (vehicles,humans, animals). (v) GPS inaccuracies and sensor noise. (vi) Real-time or near-real-time processing requirements. (vii) Reconstruction of occluded surfaces. (viii) Maintaining metric accuracy without extensive Ground Control Points (GCPs). Input Data Mandatory (i) Drone video (1080p/4K) (ii) GPS coordinates (iii) Flight metadata Optional (i) IMU data (ii) Barometric altitude (iii) Camera intrinsic parameters (iv) RTK/PPK corrections Potential Applications (i) Border and strategic area mapping (ii) Disaster damage assessment (iii) Urban planning and smart cities (iv) Infrastructure inspection (v) Construction progress monitoring (vi) Archaeological documentation (vii) Digital twin generation (viii) Military reconnaissance and mission planning