Hyderabad Pioneers Drone-Based Urban Safety Inspections

In a significant step towards modernizing infrastructure management, Hyderabad is rolling out a groundbreaking drone-based inspection system. This UAV-powered framework, spearheaded by Prof. Harikumar Kandath, Assistant Professor at the Robotics Research Center, IIIT Hyderabad, in collaboration with IHub-Data, promises a faster, safer, and more efficient alternative to traditional manual surveys.

Prof. Kandath, who joined IIIT Hyderabad in 2020 after earning his Ph.D. from IISc Bangalore and completing postdoctoral research at NTU Singapore, specializes in aerial robotics, multi-robot systems, and control theory. His expertise in UAV autonomy, robust estimation, and fault detection forms the foundation of this innovative inspection framework.

The project also benefits from key contributions by Prof. Ravi Kiran S, who developed the computer vision algorithms, and Prof. R. Pradeep Kumar, who provided insights into structural parameters. These experts ensure that the system's evaluations align with real-world civil engineering principles.

Traditional building inspections often involve physical risks, extensive manpower, and significant time investments. This new drone-integrated system streamlines the entire process, from aerial data capture to intelligent analysis. By leveraging high-resolution UAV imagery combined with automated computer vision algorithms, the project aims to redefine how cities monitor structural health, spatial compliance, and architectural features.

At the heart of this innovation lies a suite of seven intelligent modules, each designed to address specific aspects of structural evaluation. The Distance Module accurately measures the gap between adjacent buildings, ensuring adherence to urban planning norms and building safety regulations. This is particularly useful in densely populated areas where unauthorized constructions can pose significant risks.

Further enhancing structural assessment is the Plan Shape and Roof Area Estimation module, which calculates the geometry of a building's footprint and roof surface. This data is invaluable for load-bearing evaluations and assessing solar panel installation feasibility. The Roof Layout Estimation module provides insights into roof slopes, ridgelines, and segments, crucial for gauging water drainage patterns and structural soundness.

Structural safety is prioritized through the Crack Detection and Building Tilt Estimation modules. Using advanced image processing, the system can detect surface-level cracks at an early stage, enabling preemptive maintenance before major failures occur. The tilt module assesses the angular deviation or lean of a structure, vital for identifying foundational instability or seismic vulnerability.

Understanding a building's capacity and urban relevance is made easier with the Window Detection and Storey Count module. By analyzing visual data to identify windows and floor lines, it estimates the total number of floors, aiding occupancy estimates, fire safety planning, and façade analysis. The Storey Height Estimation module calculates the average vertical height per floor, which is crucial for evacuation planning and digital urban modeling.

This integrated system is not just a toolkit of algorithms but a complete UAV-based pipeline, beginning with drone-assisted image capture and culminating in automated structural insights. A user-friendly graphical interface (GUI) is currently under development, which will soon enable engineers, architects, and city planners to interact with visual analytics and reports in real-time.

The project has already begun demonstrating its practical benefits in urban pilot studies, particularly in areas requiring periodic compliance checks and pre-construction validations. With India’s cities expanding rapidly, such smart surveillance tools could play a pivotal role in creating more resilient, efficient, and data-driven urban ecosystems.

For more detailed technical documentation and module breakdowns, visit the official project documentation.