Abstract

Particulate matter (PM) is considered the primary contributor to air pollution and has severe implications for general health. PM concentration has high spatial variability and thus needs to be monitored locally. Traditional PM monitoring setups are bulky, expensive and cannot be scaled for dense deployments. This paper argues for a densely deployed network of IoT-enabled PM monitoring devices using low-cost sensors. In this work, 49 devices were deployed in a region of the Indian metropolitan city of Hyderabad out-of this, 43 devices were developed as part of this work and 6 devices were taken off the shelf. The low-cost sensors were calibrated for seasonal variations using a precise reference sensor. A thorough analysis of data collected for seven months has been presented to establish the need for dense deployment of PM monitoring devices. Different analyses such as mean, variance, spatial interpolation and correlation have been employed to generate interesting insights about temporal and seasonal variations of PM. In addition, event-driven spatiotemporal analysis is done for PM values to understand the impact of the bursting of firecrackers on the evening of the Diwali festival. A web-based dashboard is designed for real-time data visualization.

Abstract

Greenhouse gases (GHGs) play an important role in controlling local air pollution as well as climate change. In this study, we retrieved column-averaged dry-air (X) mole fractions of carbon dioxide (CO2), methane (CH4), and carbon monoxide (CO) using a ground-based EM27/SUN Fourier transform infrared spectrometer (FTIR). The EM27/SUN spectrometers are widely in use in the COllaborative Carbon Column Observing Network (COCCON). The PROFFAST software provided by COCCON has been used to analyze the measured atmospheric solar absorption spectra. In this letter, the diurnal variation and the time series of daily averaged XCO2, XCH4, and XCO covering the period from December 2020 to May 2021 are analyzed. The maximum values of XCO2, XCH4, and XCO are observed to be 420.57 ppm, 1.93 ppm, and 170.40 ppb, respectively. Less diurnal but clear seasonal changes are observed during the study period. XCH4 and XCO from the Sentinel-5Precursor (S5P)/TROPOspheric Monitoring Instrument (TROPOMI) are compared against the EM27/SUN retrievals. The correlation coefficient for the EM27/SUN retrieved XCH4 and XCO, with the S5P/TROPOMI products, are 0.75 and 0.94, respectively

Abstract

Automated generation and (user) authoring of the realistic virtual terrain is most sought for by the multimedia applications like VR models and gaming. The most common representation adopted for terrain is Digital Elevation Model (DEM). Existing terrain authoring and modelling techniques have addressed some of these and can be broadly categorised as: procedural modeling, simulation method, and example-based methods. In this paper, we propose a novel realistic terrain authoring framework powered by a combination of VAE and generative conditional GAN model. Our framework is an example-based method that attempt to overcome the limitations of existing methods by learning a latent space from real world terrain dataset. This latent space allows us to generate multiple variants of terrain from a single input as well as interpolate between terrains, while keeping the generated terrains close to real world data distribution. We also developed an interactive tool, that lets the user generate diverse terrains with minimalist inputs. We perform thorough qualitative and quantitative analysis and provide comparison with other SOTA methods. We intend to release our code/tool to academic community.

Abstract

This paper presents the application of computer vision and machine learning to autonomous approach and landing and taxiing for an air vehicle. Recently, there has been growing interest in developing unmanned aircraft systems (UAS). We present a system and method that uses pattern recognition which aids the landing of a UAS and enhances the human-crewed air vehicle landing. Auto-landing systems based on the Instrument Landing System (ILS) have already proven their importance through decades. The auto-land systems work in conjunction with a radio altimeter, ILS, MLS, or GNSS. Closer to the runway, both under VFR and IFR, pilots are expected to rely on visual references for landing. Modern systems like HUD or CVS allow a trained pilot to manually fly the aircraft using guidance cues from the flight guidance system.Notwithstanding the type of landing and instruments used, typically, Pilots are expected to have the runway threshold markings, aiming point, displacement arrows, and touch down markings/lights insight before Minimum Decision Altitude (MDA). Imaging sensors are the essential standard equipment in crewed and crewless aerial vehicles that are widely used during the landing maneuver. In this method, a dataset of visual objects from satellite images is subjected to pattern recognition training. This trained system learns and then identifies and locates important visual references from imaging sensors and could help in landing and taxiing.

Abstract

Defining spatial distribution of airborne volcanic ash in the neighbourhood of an erupting volcano is a synoptic scale problem, severely impacting lives and livelihoods. Robust algorithms are needed to model such complex phenomenon from sparse field data. This study investigated optimal modelling of the spatial dispersion of ash using Empirical Bayesian Kriging (EBK): a geostatistical, probabilistic algorithm. Both distance and ash temperature values of samples from the 2010 Icelandic eruption were spatially correlated using semivariograms to generate prediction and error surfaces. Results showed that block averages were 90% accurate as validated against NCEP NWP model data. The work supports the utility of EBK in datasets where spatial autocorrelation is not significant. Furthermore, the results could help generate risk maps to delineate safety zones for aircraft

Abstract

Trees are an integral part of many outdoor scenes and are rendered in a wide variety of computer applications like computer games, movies, simulations, architectural models, AR and VR. This has led to increasing demand for realistic, intuitive, lightweight and easy to produce computer-generated trees. The current approaches at 3D tree generation using a library of trees lack variations in structure and are repetitive. This paper presents an extended grammar-based automated solution for 3D tree generation that can model a wide range of species, both Western and Indian. For the foliage, we adopt a particle system approach that models the leaf, its size, orientation and changes. The proposed solution additionally allows control

Abstract

During the COVID-19 pandemic, India’s complete lockdown was implemented from March 24 to May 3 2020, to minimize the effects of community transfer and control the rapidly growing rate of the virus spread. In this paper, we focus on quantifying the change in air pollution due to Hyderabad’s lockdown, the capital of Telangana State. For this, two datasets are employed. The first dataset is from the Central Pollution Control Board (CPCB) stations in the city. In contrast, the second dataset is the dense IoT network of PM monitors deployed in the educational campus of IIITH in Gachibowli, Hyderabad. An analysis is done on the collected data to understand the effect of lockdown on PM values while considering the yearly and seasonal variations. It has been shown that while there has been a significant drop in PM values. However, through correlation analysis between the temperature and the PM values during the regular times, not all PM values decrease because of the lockdown. Index Terms

Abstract

Generation of spatial profiles of airborne volcanic ash that is spread at synoptic scales is a problem that directly impacts lives and properties. Robust algorithms are needed to model the distribution using sparse data sampled in the neighborhood of an erupting volcano. Existing Numerical Weather Prediction (NWP) algorithms model the dispersion at coarser spatial resolutions. In this study, we evaluate a geospatial interpolation technique called Kriging [1] to generate prediction and error surfaces. Location and temperature values of ash from 2010 Icelandic eruption were spatially autocorrelated using a stochastic kriging method, known as Empirical Bayesian Kriging (EBK) [7]. The EBK estimates were rigorously validated against NWP for regions with varying sample densities. Subsequently, a method to generate an accurate overlay map using EBK estimates to augment NWP outputs is proposed to aid in …

Abstract

This paper presents the application of computer vision and machine learning to autonomous approach and landing and taxiing for an air vehicle. Recently, there has been growing interest in developing unmanned aircraft systems (UAS). We present a system and method that uses pattern recognition which aids the landing of a UAS and enhances the human-crewed air vehicle landing. Auto-landing systems based on the Instrument Landing System (ILS) have already proven their importance through decades. The auto-land systems work in conjunction with a radio altimeter, ILS, MLS, or GNSS. Closer to the runway, both under VFR and IFR, pilots are expected to rely on visual references for landing. Modern systems like HUD or CVS allow a trained pilot to manually fly the aircraft using guidance cues from the flight guidance system. Notwithstanding the type of landing and instruments used, typically, Pilots are expected to have the runway threshold markings, aiming point, displacement arrows, and touch down markings/lights insight before Minimum Decision Altitude (MDA). Imaging sensors are the essential standard equipment in crewed and crewless aerial vehicles that are widely used during the landing maneuver. In this method, a dataset of visual objects from satellite images is subjected to pattern recognition training. This trained system learns and

Abstract

In developing the infrastructure facilities such as irrigation canals and road networks, topography acts as a significant enabler or constraint. Contour maps and low resolution DEMs have been used by Irrigation engineers and planners to assess the canal routing options, which is time consuming and requires repeated evaluations of the potential paths. So, there is a need to develop robust path planning algorithms, including least cost routing, that takes the topographic and engineering constraints while providing potential canal routing paths. Some recent works have attempted to develop algorithms on synthetic data sets but have not been scaled up on high-resolution data sets, limiting their practical use. This work develops a generic algorithm to determine the least-cost flow path between two geo-locations, given the grid-based Digital Elevation Models (DEMs) and a unit cost of construction per length. From the numerous paths that are possible between the two points in any given topography, a distinct least-cost path is identified. The proposed approach is evaluated by computing canal routing paths over publicly available real-world datasets across two different resolutions of 1Km and 90-meter from different sources for Indian terrains. This is then compared against a global reference real-world dataset, Digital Chart of the World (DCW) at 1Km at a grid cell level. The results across multiple stretches shows an average accuracy of 82.09% as measured based on the path overlap against the DCW dataset, proving that the proposed algorithm can be useful in practice.