Abstract

Accurate runway extraction from very high-resolution satellite imagery is challenging: runways are extremely elongated and key
surface markings (e.g., threshold bars) are tiny relative to the scene.
Most existing methods yield coarse masks, limiting downstream use. We
present AERO-DETR, a dual-perspective pipeline that couples a topdown runway locator with a bottom-up sparse-marking detector, and
introduces orientation-normalized training (per-runway chips and
labels rotated to horizontal) to stabilize pattern learning under severe domain variability (geographic diversity, acquisition conditions, extreme aspect ratios, and near-parallel look-alikes). On sub-meter imagery, AERODETR achieves mAP@50:95=0.572, outperforming the best baseline
(DETR, 0.369) by +0.203 absolute (+55% relative) under identical
tiling, augmentation, and epochs, and attains a mean polygon IoU of
0.8927 against ground truth across single, parallel, intersection, mixed,
and complex airport layouts. These results indicate that fusing marking
localization with extent refinement is effective for precise runway extraction.

Abstract

This paper focuses on the construction of water
consumption patterns in Intermittent Water Supply (IWS) systems. IWS systems supply water at specific, requiring consumers to store it for non-supply hours. In IWS systems, the lack of high-resolution data makes it challenging to construct accurate instantaneous, daily, or weekly consumption patterns. Most studies focus on individual consumption to estimate per capita end-use, leaving per-building consumption patterns unaddressed. This paper proposes two Internet of Things (IoT) based, efficient,
scalable, and cost-effective strategies to capture water consumption in IWS systems at the consumer end. The proposed strategies incorporate innovative methodologies, including the utilization of
flow and water level data for monitoring water consumption. A bottom-up approach was employed to develop pertinent water consumption patterns based on these strategies. A data collection
campaign was organized to capture the water consumption of an educational institute serviced by IWS, with water consumption data recorded every 3 minutes. Approximately 125,000 data
points were collected and analyzed across five buildings, including Hostel and Classroom Block. The water consumption for all end-user categories within the campus was normalized using peak factors to compare the volatility in the water consumption among
them. Water demand curves that explain occasional events, as well as daily and weekly periodicities, were also constructed. This paper aids in assessing water needs, informed decision making and reducing wastage by enabling upgrades to smart building
infrastructure, even in IWS settings.

Abstract

Airborne PM₁₀ poses significant respiratory and cardiovascular risks to the vulnerable population. In high-elevation urban zones like Gachibowli, Hyderabad, understanding the spatiotemporal behaviour of PM₁₀ spread is essential to develop targeted health advisories. This is not achievable solely by increasing the number of monitoring stations. The local topography, Land Use/Land Cover (LULC) patterns and mixing conditions must be understood to identify pollution sources and dispersal dynamics.

We estimated PM₁₀ concentrations based on Aerosol Optical Depth from two satellite sources - half-hourly, INSAT-3D geostationary satellite data at 11km; and daily polar-orbiting, MODIS Aqua data at 3km spatial resolution. By validating these PM₁₀ estimates against government-monitored Central Pollution Control Board (CPCB) sensors and low-cost academic AirIOT sensors of IIIT-Hyderabad, a near-continuous and hyperlocal PM₁₀ concentration estimate, near the ground surface, has been generated.

Among the 4 Machine learning models evaluated, Multiple Linear Regression technique offered the best results when temperature, humidity and ERA-5 Planetary Boundary Layer Height were used as predictors. While satellite-based PM₁₀ estimate averages ranged from 61-72 µg/m³, AirIOT and CPCB ranged 24-57 µg/m³ and 99-106 µg/m³ respectively, during colder months. While AirIOT sensors are spread across varied microenvironments, the CPCB node lies near a main road with partial green cover. Satellite-derived values provide frequent, broad-scale estimates of pollutant distribution, and AirIOT and CPCB give point measurements. This resolution gap drives our effort to bridge scales and produce PM₁₀ estimates at ~1-2 km resolution with temporal continuity, while also capturing sub-grid variability and dissipation patterns.

Thus, this integrated framework allows for hyperlocal pollution mapping for health-related early warning systems even in areas with sparse PM₁₀ sensor coverage. By correlating PM₁₀ subtypes with LULC and dispersion, health outcomes can be traced to specific exposure types. By extending this method across India, to megacities like New Delhi, a health risk classification can be developed along with guidelines for clinical response measures.

Abstract

Contributing catchment land use patterns under rapid urbanization, agricultural practices and climatic variability in tropical regions significantly influence the sediment load of inland waterbodies such as reservoirs. This study presents a modeling framework to estimate qualitative turbidity dynamics in two tropical reservoirs (Bhadra and Tungabhadra) within same river basin but different landscape characteristics, using Sentinel-2 satellite data from 2016 to 2021 using Google Earth Engine (GEE). The framework demonstrates a robust capability of Sentinel-2 data, in detecting, mapping spatiotemporal trends and classifying turbidity levels from low to high along with impact of potential catchment characteristics. The Normalized Difference Turbidity Index (NDTI) values have a strong correlation with in-situ data (Earth Observing and Mapping EOMAP and field-based observations) having values > 10 NTU (nephelometric turbidity unit) with R2 = 0.81 and standard Error Estimate (SEE) of 5.3 respectively. In addition, the correlation between red band reflectance values with <10 NTU, has demonstrated a strong relation with an R2 and SEE of 0.74 and 1.7 respectively. Thus, the combination of NDTI and single red band reflectance values were used further for classifying the different turbidity levels (low, moderate and high). It was also observed that diverse land use patterns, particularly high proportions of urban and agriculture area along with rainfall, impact the annual and seasonal fluctuation of turbidity level in the reservoirs. The study demonstrates that remote sensing (RS) approach can effectively estimate turbidity levels in tropical reservoirs, highlighting the impact of catchment characteristics and supporting improved monitoring and sustainable water resource management.

Abstract

The land use, land-use changes, and forestry (LULUCF) sector plays
a significant role in increasing atmospheric carbon dioxide (CO2) in the
current climate change scenario. The present study assessed the longterm CO2 emissions within the LULUCF sectors across India's climatic
zones as described by Köppen. The continuous combustion of fossil
fuels has led to a rise in CO2 levels by 53 parts per million (ppm),
increasing from 372 to 425 ppm in the Indian region between 2002 to
2022. The investigation reveals a consistent upward trend in CO2 levels
across Köppen climatic zones. The semi-arid (steppe) climate zone
shows an increasing trend of 2.18 ppm year−1, whereas Tundra exhibits
a trend of 2.32 ppm year−1. The rate of change of atmospheric CO2 for
India is 2.20 ppm year−1. A regional correlation exists between CO2
concentration and elevated anthropogenic emissions activities in the
Indo-Gangetic Plains (IGP). Settlements growth has occurred across
various climatic zones, with the Subtropical Highland zone showing
the most significant rise at 54.84%. The findings underscore the urgent
requirement for sustainable land use practices. This study utilized
multivariate linear regression analysis to investigate different CO2
sources across LULUCF classes, using these as independent variables
within Köppen climatic zones. The evaluation focused on identifying
the classes with the highest and lowest contributions to CO2 emissions
throughout the study period. Further training was carried out on
different models for India as a whole and by climatic zone, revealing
that the settlement class is a significant source of CO2 emission. In
contrast, the forest class acts as a substantial sink for CO2.

Abstract

A significant task that municipalities or equivalent government organizations across the world perform is street addressing including naming of roads. Due to unstructured ways in which towns and cities grow, this process needs significant thought and effort which in reality results in major delays in terms of action taken on these issues. Meanwhile, the local populace which needs some naming convention for practical needs may take the initiative to arrive at reasonable names for the roads within their vicinity based on their local knowledge. This can result in significant sections of the road network without an official name or named after a landmark or a famous personality in many places across the world. The past 2-3 decades have seen an increasing digitisation of government activities. However, a lot of the street addressing and road naming systems and conventions were derived before the digitisation era, which in most cases means that computational parsability of the addresses or road names that would be important for digital processing would not be accounted for. There has been a significant increase in importance and usage of location-based geocoding services like what3words, Zippr and plus codes in recent times. However, these services provide alphanumeric code-based addressing which is not intuitive for human understanding, lacks directionality, does not account for existing address information nor the geometric relations of road topology. In this paper, we propose usage of (modified versions of) well known AI search algorithms for automated road numbering and evaluate them based on spatial proximity and connectivity on roadmaps of three different cities.

Abstract

Infinite terrain generation is an important use case for
computer graphics, games and simulations. However, current techniques are often procedural which reduces their realism. We introduce a learning-based generative framework
for infinite terrain generation along with a novel learningbased approach for level-of-detailing of terrains. Our
framework seamlessly integrates with quad-tree-based terrain rendering algorithms. Our approach leverages image
completion techniques for infinite generation and progressive super-resolution for terrain enhancement. Notably, we
propose a novel quad-tree-based training method for terrain enhancement which enables seamless integration with
quad-tree-based rendering algorithms while minimizing the
errors along the edges of the enhanced terrain. Comparative evaluations against existing techniques demonstrate
our framework's ability to generate highly realistic terrain
with effective level-of-detailing.

Abstract

Atmospheric methane (CH4) is a potent climate change agent responsible for a fraction of global
20 warming. The present study investigated the spatial and temporal variability of atmospheric
column-averaged (X) CH4 (XCH4) concentrations using Greenhouse gases Observing SATellite
(GOSAT) and TROPOspheric Monitoring Instrument onboard the Sentienl-5 Precursor
(S5P/TROPOMI) data from 2009 to 2022 over the South Asia region. During the study period, the
long-term trends in XCH4 increased from 1700 ppb to 1950 ppb with an annual growth rate of 8.76
ppb year-1 25 . Among all natural and anthropogenic sources of CH4, the rate of increase in XCH4 was
higher over the Mundra thermal power station and Mundra ultra mega power plant
at about 9.62 ppb year-1
, followed by the coal site at about 8.76 ppb year-1
(Korba). With a growth
rate of 8.61 ppb year-1
, the Sundarbans natural wetland competes with coal sites, producing over
30 MT, indicating an equivalent anthropogenic source. For the 15 Indian Agroclimatic zones,
30 significant high emissions of CH4 were observed over the Middle Gangetic Plains (MGP), Trans
Gangetic Plains (TGP), Upper Gangetic Plains (UGP), East Coast Plains & Hills (ECPH), Lower
Gangetic Plains (LGP) and East Gangetic Plains (EGP). Further, the bottom-up anthropogenic CH4
emissions data are mapped against the XCH4 concentrations and found high correlation in the Indo
Gangetic Plains (IGP) region, indicating the hotspots of anthropogenic CH4. The present study
35 highlighted the impact of natural and anthropogenic sources of XCH4 and quantified the spatiotemporal changes in XCH4 at each study site over the Indian region

Abstract

The present study investigated the effects of land use/land cover (LU/LC) changes on atmospheric carbon dioxide (CO2) and methane (CH4) concentrations over the sub-urban region of India (Shadnagar) using continuous decadal CO2 and CH4 in-situ data measured by the greenhouse gas analyser (GGA). Data was collected from 2013 to 2022 at a 1 Hz frequency. Analysis of the current study indicates that during pre-monsoon, the seasonal maximum of CO2 was 409.91 ± 9.26 ppm (μ ± 1σ), while the minimum during monsoon was about 401.64 ± 7.13 ppm. Post-monsoon has a high seasonal mean CH4 concentration of 2.08 ± 0.06 ppm, while monsoon has a low seasonal mean CH4 concentration of 1.88 ± 0.03 ppm. The primary classes, such as forest, crop, and built-up, were considered to estimate the effect of LU/LC changes on atmospheric CO2 and CH4 concentrations. Between 2005 and 2021, the study's results show that the built-up area at radii of 10 km, 20 km, and 50 km increased by 0.17 %, 0.10 %, and 0.4 %, respectively. While other LU/LC categories declined by 30 %, agriculture areas increased by 30 % on average. As a result, the CO2 and CH4 concentrations at the study site are increased by 6 % (26 ppm) and 6.5 % (140 ppb), respectively. The present study utilised the fire-based carbon emissions data from the Global Fire Emissions Database (GFED) to understand the impact on atmospheric CO2 and CH4. Analysis of the present work investigated the influence of transported airmass on CO2 and CH4 during the pre-monsoon and post-monsoon seasons using the HYSPLIT trajectories and found emissions were from the northwest, southeast, and northeast of the study site. Further, in-situ CO2 and CH4 records are compared against the MIROC4-ACTM simulation, and strong agreement was found with bias of 1.80 ppm and 0.98 ppb for CO2 and CH4, respectively.

Abstract

Particulate matter (PM) is a critical air pollutant with severe health implications, yet existing stationary monitoring networks often fail to capture its complete spatial and temporal variability in urban environments. This paper presents a novel approach to city-wide air quality monitoring using low-cost sensors mounted on mobile platforms. To validate this method, a six-month field study was conducted in Hyderabad, India, deploying IoT-enabled devices on four college buses. These mobile devices capture PM concentrations, temperature, relative humidity, GPS coordinates, and vehicle speed twice daily across different parts of the city. The primary objective was to demonstrate the necessity of mobile air pollution monitoring to identify PM variability across diverse urban areas. The data revealed significant variations in PM concentrations across different parts of the city and seasons, highlighting the impact of local activities on air quality. The study examines seasonal trends, area-specific variations, and temporal patterns of PM concentrations, identifying pollution hotspots within the city. It shows how important it is to provide up-to-date, location-specific air quality information to people with pollution-related health issues.