Farmers struggle with fair irrigation turn allocation, access to government support, and targeted interventions. Participants must build a digital agricultural support ecosystem that could include digital Parchi/Bari allocation for irrigation turns, tools to analyze Kisan registry data, and systems that help the government provide more focused schemes. The goal is to reduce disputes, improve resource distribution, and use farmer data for impactful decision-making.

Healthcare systems need smoother data integration and faster emergency response. Participants must design solutions that link ABHA IDs with lab reports, prescriptions, and other medical records for seamless health data access. Additionally, the challenge includes optimizing ambulance utilization—analyzing response times, network inefficiencies, and suggesting better deployment strategies. The aim is to build a more connected, responsive healthcare ecosystem.

Road infrastructure suffers from inconsistent monitoring, manual inspection delays, and lack of reliable data. This challenge invites participants to build smart solutions that can assess newly constructed roads, evaluate DLP roads using serial photos or videos, identify potholes, detect damaged stretches, and analyze accident-prone zones. Teams can also propose tools for identifying congestion hotspots and recommending corrective actions. The goal is to use AI, IoT, GIS, or mobile-based systems to improve quality checks, maintenance cycles, and road safety.

Cities possess thousands of private CCTV cameras in shops, societies, and commercial spaces. However, there is no unified system for authorities to leverage this infrastructure during emergencies or investigations. Participants must create a secure, privacy-controlled platform that allows voluntary CCTV feed sharing with local authorities. This system can help detect incidents faster, reduce crime, and improve overall surveillance coverage.

Rajasthan receives millions of domestic and international tourists every year, but the state lacks a unified, accurate, and real-time mechanism to measure how many visitors arrive, where they go, and how tourism infrastructure is being utilized. Currently, data on tourist footfall at monuments, museums, parks, and other attractions is either manually recorded, inconsistent, or not collected at all. Similarly, hotel information—such as rating, accommodation size, district-wise distribution, and availability—exists across multiple systems without a centralized analytics framework.

The challenge is to build a comprehensive Tourism Intelligence System that can:

• Accurately count and differentiate domestic and international tourists entering Rajasthan.
• Measure real-time footfall at major tourist locations using digital, sensor-based, or AI-driven methods.
• Aggregate and analyze hotel data (ratings, accommodation capacity, district/location spread) to understand how tourism infrastructure is distributed and utilized.
• Identify tourism trends, peak seasons, under-utilized destinations, and demand gaps across regions.
• Provide dashboards or insights that help government departments make strategic decisions about marketing, infrastructure development, crowd management, and tourism policy.

Participants can explore solutions using IoT, computer vision, digital identity, ticketing system integrations, mobile-based reporting, hotel APIs, or any innovative approach that enables reliable, scalable, and actionable tourism analytics for Rajasthan.

Cities face frequent civic issues like potholes, waste accumulation, broken streetlights, and animal carcasses on the road. Reporting is slow, manual, and often unclear about responsibility. Participants must develop a unified reporting system where citizens can upload issues with location and photos, and municipalities can manage workflows, track resolution, and ensure accountability. This includes features for reporting non-functional streetlights, tracking maintenance staff attendance, and monitoring cleanliness of public toilets and govt facilities.

Problem Statement 1: Inequitable Water Distribution in Urban Areas

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Challenge Context

Urban centers in Rajasthan face significant disparities in water distribution across supply zones. Certain areas receive excessive water while others face low pressure or intermittent supply. PHED’s current operation largely depends on static valve schedules and manual monitoring, resulting in public dissatisfaction, network stress, and inefficient resource use.

Problem Definition

There is no dynamic, data-driven mechanism to ensure equitable water distribution in real-time. Lack of integration between field data (flow, pressure, consumption) and operational decision-making prevents equitable supply management.

Innovation Opportunity

Participants are challenged to design an AI + IoT-driven platform that ensures real-time equitable water distribution across the entire urban water network.

Expected Solution Features

1. AI/IoT-driven Decision Support System to:

• Collect real-time data from pressure and flow sensors.
• Identify imbalances and optimize valve operations automatically.
• Generate equitable distribution schedules.

2. Web-Based Hydraulic Simulation Tool for:

• Modeling network behavior under varying demand and source conditions.
• Testing alternate supply scenarios before field implementation.
• Providing visual dashboards for planners and engineers.
• Gives a tentative estimates with reference to the departmental Basic Schedule of Rate (BSR).

3. Performance Dashboards for supervisors to monitor equity, losses, and grievances.

Expected Outcomes

• Real-time, equitable water supply across zones.
• Reduction in water losses and public complaints.
• Evidence-based network optimization through hydraulic simulation.

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Problem Statement 2: Lack of AI-Driven Integrated GIS Platform for Predictive Analysis and Future Planning

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Challenge Context

PHED manages a vast network of pipelines, pumping stations, reservoirs, and treatment plants across Rajasthan. Yet, data is fragmented, and there is no unified GIS platform to visualize, analyze, or predict network performance. As a result, issues like pipeline bursts, contamination events, and capacity constraints are addressed reactively.

Problem Definition

Current systems lack spatial integration and predictive capabilities. Without geospatial intelligence, PHED cannot anticipate risks or plan infrastructure upgrades efficiently.

Innovation Opportunity

Participants are invited to develop an AI-powered Integrated GIS Platform that brings together asset, sensor, and spatial data to support predictive maintenance, pollution control, and infrastructure planning.

Expected Solution Features

1. AI-Powered GIS Integration to:

• Digitize, geotag, and visualize all PHED assets.
• Integrate IoT data (pressure, flow, quality) for live network status.
• Apply ML algorithms for predictive maintenance and pollution risk analysis.

2. Digitization & Geo-referencing Toolkit to:

• Convert non-GIS formats (AutoCAD, Shape, KML files) into geospatially referenced layers.
• Provide an interactive graphic interface to create and edit supply zones and jurisdiction boundaries.

3. Planning Support Module for scenario analysis and expansion strategy formulation.

Expected Outcomes

• Unified GIS-based asset management and predictive analytics.
• Early detection of failures and pollution incidents.
• Faster, data-backed decision-making for network planning.

Problem Statement 3: Absence of an Integrated IT Platform for DPR Generation, Tender, and Works Management

Challenge Context

PHED’s project cycle — from conceptualization to commissioning — involves multiple disjointed workflows: manual DPR preparation, isolated tendering, and separate monitoring systems. This fragmentation causes inefficiency, duplication, and delays.

Problem Definition

The absence of a unified IT system prevents seamless flow of project data across planning, procurement, and execution stages. Manual documentation and lack of standard templates reduce transparency and accountability.

Innovation Opportunity

Participants are invited to design a Comprehensive Project Lifecycle Management Platform that digitally integrates all phases — from DPR generation to work completion.

Expected Solution Features

1. DPR Generation Module

• Automated and data-driven generation using standard templates based on departmental norms.
• Integration with GIS and asset databases for accurate costing and design inputs.

2. Tender & Procurement Management

• Digital tender preparation from DPR, evaluation and publication, bid evaluation, and contractor onboarding.
• Transparent contract tracking and milestone-based monitoring.

3. Works Monitoring System

• Field reporting through mobile apps and geo-tagged progress updates.
• Real-time dashboards showing financial and physical progress.
• Integration with state systems (IFMS, eProcurement).

Expected Outcomes

• End-to-end project transparency and faster execution.
• Standardization of DPR and tender workflows.
• Improved accountability and data-driven decision-making.