Sai Ganesh Veeravalli

In growing cities, deprived neighborhoods house large numbers of residents, yet their extent and distribution remain poorly quantified, complicating implementation of SDG 11.1. 1. We present the first global, neighborhood-scale spatial estimates of morphological deprivation, covering 5,132 cities in 103 countries across Africa, Asia, and Latin America & the Caribbean (LAC) home to 3.2 billion people. Neighborhood units and built-environment indicators from the City Segments v1 dataset were combined with segment-level labels from the eight-city IDEABench benchmark to train a supervised model, which was then applied to classify each segment as morphologically deprived or non-deprived. The mapped cities contained 1.96 billion residents, of whom 349 million (17.8%) lived in deprived segments, with the highest regional shares in Africa and substantial burdens in Asia and LAC. Morphologically deprived populations spanned the urban hierarchy, with about one-third living in small and medium cities, revealing important gaps in current deprivation monitoring.

Post-approval monitoring of urban development projects remains one of the weakest links in environmental governance, particularly in fast-urbanizing contexts where institutional oversight is limited. This study examines the redevelopment of Rushikonda Hill, an ecologically sensitive coastal site in Andhra Pradesh, India, to quantify land alteration and evaluate governance implications. High-resolution Planet Scope imagery and historical Google Earth Pro basemaps from 2021 to 2023 were analysed through visual interpretation and manual digitization. The approved project footprint was 9.88 acres, yet satellite analysis indicated that by late 2023 approximately 19.1 acres had been visibly altered; nearly double the sanctioned extent. Alteration progressed in distinct phases: initial vegetation clearance, slope terracing and excavation, large-scale levelling, and visible building foundation work. Artificial green mats were later placed on exposed slopes, creating an appearance of regrowth without evidence of ecological restoration. These findings point to structural gaps in India’s post-clearance compliance system: limited independent audits, reliance on self-reporting, and conflicts of interest in state-led projects. Based on the Rushikonda case and comparable examples, three policy pathways are proposed: integrating satellite audits into compliance, legally recognizing remote sensing as admissible evidence, and expanding public-access monitoring platforms. The approach demonstrated here offers a transferable framework for using satellite imagery to detect regulatory non-compliance, strengthening environmental accountability in similar governance settings worldwide.

Coastal areas worldwide are increasingly vulnerable to natural and human interventions, necessitating systematic vulnerability assessments for effective risk management. However, conventional assessments often overlook the role of coastal protection structures, assuming uniform effectiveness or ignoring their presence. This omission can misrepresent coastal risk, particularly in regions where structural defences like seawalls and groins are the primary means of protection. This study presents Coastal Vulnerability Score (CVS) framework that integrates coastal structure status as a foundational component of vulnerability assessments. The methodology classifies coastal stretches into open coast (OC), partial damage (PD), and total damage (TD), based on the structural integrity of defences. Vulnerability is then assessed using four key parameters: wave energy, sediment characteristics, geomorphology, and population density – calculated through a weighted approach to reflect exposure levels. Applying this framework to Thiruvananthapuram district, Kerala, India, the results identify Petta (CVS: 2.63) and Muttattara (CVS: 2.52) as the most vulnerable villages due to eroding coastlines, high wave energy, failed defences, and dense population. In contrast, Cherunniyur (CVS: 1.47), Varkala (CVS: 1.56), and Idava (CVS: 1.58) exhibit low vulnerability due to intact structures and natural defences such as cliffs and accreting beaches. The CVS framework provides a data-driven approach for prioritizing interventions. High-risk villages require immediate structural reinforcements, while lower-risk areas can benefit from soft protection measures like beach nourishment. Integrating this approach into coastal governance frameworks can enhance risk-based prioritization and proactive maintenance ensuring sustainable resilience strategies. The methodology is scalable and adaptable, offering a practical tool for evidence-based coastal management.