Abstract

The current methods of object segmentation and extraction and classification of aerial LiDAR data is manual and tedious task. This work proposes a technique for object segmentation out of LiDAR data. A bottom-up geometric rule based approach was used initially to devise a way to segment buildings out of the LiDAR datasets. For curved wall surfaces, comparison of localized surface normals was done to segment buildings. The algorithm has been applied to both synthetic datasets as well as real world dataset of Vaihingen, Germany. Preliminary results show successful segmentation of the buildings objects from a given scene in case of synthetic datasets and promissory results in case of real world data. The advantages of the proposed work is non-dependence on any other form of data required except LiDAR. It is an unsupervised method of building segmentation, thus requires no model training as seen in supervised techniques. It focuses on extracting the walls of the buildings to construct the footprint, rather than focussing on roof. The focus on extracting the wall to reconstruct the buildings from a LiDAR scene is crux of the method proposed. The current segmentation approach can be used to get 2D footprints of the buildings, with further scope to generate 3D models. Thus, the proposed method can be used as a tool to get footprints of buildings in urban landscapes, helping in urban planning and the smart cities endeavour.

Abstract

Relational databases have been around for a long time and spatial databases have exploited this feature for close to two decades. The recent past has seen the development of NoSQL non-relational databases, which are now being adopted for spatial object storage and handling, too. While SQL databases face scalability and agility challenges and fail to take the advantage of the cheap memory and processing power available these days, NoSQL databases can handle the rise in the data storage and frequency at which it is accessed and processed - which are essential features needed in geospatial scenarios, which do not deal with a fixed schema(geometry) and fixed data size. This paper attempts to evaluate the performance of \ an existing NoSQL database ’MongoDB’ with its inbuilt spatial functions with that of a SQL database with spatial extension ’PostGIS’ for two problems spatial and aggregate queries, across a range of datasets, with varying features counts. All the data in the analysis was processed In-memory and no secondary memory was used. Initial results suggest that MongoDB performs better by an average factor of 10x-25x which increases exponentially as the data size increases in both indexed and non-indexed operations. Given these results, NoSQL databases may be better suited for simultaneous multiple-user query systems including Web-GIS and mobile-GIS. Further studies are required to understand the full potential of NoSQL databases across various geometries and spatial query types.

Abstract

Current methods of building segmentation from LiDAR depends on extraction of roof planes to segment the buildings. In our work, we propose a new method to extract buildings using the wall facades. The motivation of using wall facades is based on the rise of terrestrial LiDAR compared to aerial LiDAR. The shape property is the most important information obtained from a LiDAR file. We try to use this shape property from the LiDAR file to segment buildings from the given scene. In the algorithm, the non-ground return points parallel to a facade of the potential building are combined to form multiple slices. These slices are parallel to the facade of a potential building. These slices are then merged based on the shape similarity and proximity to generate the footprint of the building. After generating the footprint of the building, we can also reconstruct the 3D model of the given building. The algorithm was tested both on simulated synthetic data containing different buildings and real world benchmark datasets. The results show complete segmentation of buildings from the synthetic datasets and promissory results from the real world datasets. The algorithm proposed is versatile in nature and can be easily adapted to different datasets with minimal modifications and tweaking. Existing works involve the use of secondary datasets like satellite images, orthophotos etc in order to aid in segmentation. Our work uses only the LiDAR data and no other secondary data to identify the buildings from the scene. Demonstration have been shown to segment partial buildings in a given scene as well. Thus, the novelty of the proposed work is non-dependence on any other form of data required except LiDAR. The applications of segmenting buildings from a given LiDAR scene can be used in urban planning, resource management and monitoring tasks.

Abstract

Online WebGIS tools and services have become widely available in the recent past to visualize and query geospatial data. These are primarily spatial visualization of aggregated attribute data across various spatial scales or geo-hierarchy levels. Most of these tools lack the temporal profile of the data. Also, these are server centric. In the era of big data, the user data has various attributes collected over time across geo graphic detail. Also user may like to do visual analysis across these attributes at various levels of aggregation. Such server centric approaches give limited maneuverability to explore the data and discover hidden patterns and insights in data. This paper presents a Spatial-OLAP tool that has been developed over existing Open Source geospatial and database tools. It computes data aggregation across user-defined geo hierarchy with dynamic visualizations in a defined framework. Parameters in menu options are not pre-computed but added on the fly based on user-given data. The tool enriches user experience by combining additional data-aggregation functions over spatial too lkits like open layers and allowing user to zoom to a set of temporal charts for a more extensive analysis of data across space and time. To show the utility of this tool, a GeoBI case study was done on a large online retail data of three products sold in different countries during January 2009. The tool helped quickly identify the top five countries with maximum sales and the payment options in decreasing order of popularity. Also, relationships between quantity of product sold across the geographic regions was also extracted. As Online retailers need to draw insights from data to understand their customers across regions, we hope this tool can benefit them in finding such answers - what their popular products are, identifying regions where there is a demand for these, understanding regional preferences and many others.

Abstract

Strong earthquakes cause a great damage to buildings and other infrastructure. It is difficult to get an overview of the situation immediately after an earthquake, to facilitate the initiation of appropriate emergency and rescue measures. Shake map provides early information on the intensity of ground shaking in a location. This data will be useful to help authorities and rescue workers for planning their work. For the purpose of study, Vijayawada city, India has been selected to generate shake map. Peak ground accelerations (PGA) are generated using ground motion prediction equations (GMPE) and the shaking intensities are also estimated for Vijayawada city. A tool for shake map has been generated using php applications. Map generation is automatically triggered by any earthquake in the vicinity of Vijayawada region and the map will be available for the public and government for post-earthquake decision making.

Abstract

etties are one of the most important structures in the coastal area, which can be used for transporting large quantities of goods and raw materials from one place to the other. Their functionality is very much essential because they are lifeline structures of the country. It is observed during the past earthquakes that jetties have been damaged even under mild shaking. Damaged and unserviceable jetties cause delay of export and import business. It directly affects the economy of particular region in terms of business, employment and growth. This clearly indicates the need to design these facilities so that they can withstand natural disasters particularly earthquakes and tsunamis. In this paper, a study has been carried out to find out the damage (D) to the Jetty. The Jetty is modeled using 2D Applied Element Method (AEM) to perform damage analysis of structure. Pushover analysis is done to get base shear vs roof displacement of building using displacement control method. Using the dissipated energy approach, damage is quantified at every displacement level and normalized to 1. A fragility curve has been developed to quantify the damage of Jetty with respect to different peak ground accelerations. The damage values were calculated for the PGA values of KHF Mandvi, NKF Jodiya and KMF Jhangi and found that the Jetty got light damage (D= 0.2), and moderate damage (D= 0.38 and 0.42) respectively.

Abstract

In recent past due to rapid growth of Indian cities, there is a tremendous increase on housing industry, especially in seismic Zone IV & V. As most of these constructions are without earthquake resistant measures, the built environment in these zones has been found seismically vulnerable. Detailed seismic vulnerability evaluation is a technically complex and expensive procedure and can only be performed on a limited number of buildings. The present paper highlights the use of Rapid visual screening (RVS) method for seismic assessment of reinforced concrete (RC) buildings in Himachal Pradesh state. A RVS survey has been carried out on 1540 RC buildings, out of which 6 buildings are selected for detailed analysis. The selected buildings are modeled and analyzed through numerical approach. A correlation graph has been constructed between peak ground acceleration (PGA) and, RVS, indicating different damage states of a building. Around 80% of RC buildings located in Zone V of Himachal Pradesh will suffer moderate damage and around 73% of RC buildings located in Zone IV will suffer slight damage in the event of earthquake with PGA prescribed as per IS 1893.

Abstract

Drastic land use land cover changes (LULC) in forest landscape alters the functional ability of forest ecosystem, evident from decline in quantity and duration of water flow in streams, loss of carbon sequestration potential, etc. affecting the local livelihood, while inducing changes in the climate. This necessitates inventorying, mapping and monitoring of LULC dynamics to evolve appropriate location specific mitigation strategies. The LULC dynamics can be assessed effectively through free and open source software using temporal remote sensing data and modeling and forecasting techniques. The current research work aims to identify spatio-temporal land use changes from 2007-2016 in Kaiga Nuclear Power house (NPH) and its environs with the help of GRASS GIS (a robust free and open source software). Land uses in 2022 is predicted through Cellular Automata–Markov Chain model with the knowledge of land use transitions during the four decades. Validation of the model is done using Kappa indices, which shows good accuracy due to agreement of the predicated and actual land uses. The region has lost forest cover from 23 to 16.81 % (2016-2022) with an increase of built-up are of 42.01 to 46.81 % (2016-2022). The land use modeling, planning and policy by implying ecological science perspectives can improve understanding of ecological processes. The recent study reports of higher instances of cancer in humans with the functioning of NPH during the last decade. Any further expansion of NPH area would have serious ecological implications due to loss of vast tracts of forests, farmlands and would threaten the health and livelihood of local population.

Abstract

Earthquake resistant design of structures requires the knowledge of quantified seismic hazard. To quantify the seismic hazard, Peak Ground Acceleration (PGA) is used for design procedures. The maximum possible PGA of a particular region depends on seismic parameters like magnitude, hypocentral distance, type of soil etc. Though it can be obtained by observing the past seismic events, it is more realistic to relate it with the parameters that influence it. Due to high seismic activity at the Himalayan region, this paper is aimed at developing a Ground Motion Prediction Equation (GMPE) for this region. For this purpose, two sets of 168 earthquakes (2.5-7.2 Mw) have been selected that are recorded at different stations within 500 km radius along the Himalayan belt. A statistical analysis is carried out on both sets separately based on the relation of PGA with magnitude, hypocentral distance and the type of soil. New GMPE equations have been proposed. The same are compared with the existing GMPE for the same region and seismically similar regions.

Abstract

With the announcement of smart cities, construction of tall buildings is booming in India. There are quite a few codes for tall buildings and many of them have shortcomings in addressing the parameters for seismic design. One such parameter is the fundamental natural period of tall buildings. The expression of the fundamental period is originally developed based on Californian earthquakes and adopted by many seismic codes around the world; including current, Indian seismic code IS 1893: 2002. This paper aims to study the reliability of empirical expression of the fundamental period for tall buildings in India. For this purpose, ambient vibration tests have been carried out for 21 RC buildings, located in Mumbai and Hyderabad cities, by placing vibration sensor on topmost accessible floor. The measured periods have been compared with the code provisions. It is found in the study that as the height of the building increases, natural period is not linearly proportional to height; rather it is becoming flexible. Hence there is an urgent need for revision of the empirical expression.