Path Features in Spatial Data Generation

2014 ◽  
pp. 465-471
Author(s):  
Tomasz Płuciennik ◽  
Ewa Płuciennik
Keyword(s):  
Spatial Data ◽  
Data Generation

scholarly journals COLLABORATION AND OUTREACH TOWARDS USE OF GEOSPATIAL TECHNOLOGIES IN INDIA WITH AN EMPHASIS ON ISRO’S EFFORT

2018 ◽  
Vol XLII-5 ◽  
pp. 51-57
Author(s):  
S. Ghosh ◽  
A. Sachan ◽  
G. Srinivas
Keyword(s):  
Remote Sensing ◽  
Spatial Data ◽  
Technology Development ◽  
Economic Benefits ◽  
Data Generation ◽  
Geospatial Technologies ◽  
Policy Makers ◽  
Partnership Development ◽  
E Learning ◽  
Indian Industries

<p><strong>Abstract.</strong> Geospatial technology is adopted in India through ‘Digital India’, for better governance and socio-economic benefits of common citizens. Remote sensing imageries and derived products are a direct input for geospatial data generation, monitoring resources and providing citizen services. As Indian Space Research Organisation (ISRO) is responsible for technology development, launch and data distribution of Indian remote sensing satellites, there is a conscious effort for partnership development with international space agencies and Indian industries to fulfil this requirement. Simultaneously ISRO’s co-operation with government departments for geo-spatial data generation and utilization is taken as a priority. This Paper presents the effort of ISRO for cooperation with International and national agencies as well as with government departments and policy makers. In this surge of utilization of geo-spatial techniques capacity building has become very relevant. A glimpse of geospatial education through government &amp;amp; academic institutes in India and E-learning is presented here with an emphasis of ISRO’s contribution.</p>

scholarly journals SDG Indicator 11.3.1 and Secondary Cities: An Analysis and Assessment

2021 ◽  
Vol 10 (11) ◽  
pp. 713
Author(s):  
Melinda Laituri ◽  
Danielle Davis ◽  
Faith Sternlieb ◽  
Kathleen Galvin
Keyword(s):  
Sustainable Development ◽  
Spatial Data ◽  
Urban Form ◽  
City Planning ◽  
Population Data ◽  
Growth Patterns ◽  
Data Generation ◽  
Middle Income ◽  
Global Issues ◽  
Land Use Efficiency

Secondary cities are rapidly growing areas in low- and middle-income countries that lack data, planning, and essential services for sustainable development. Their rapid, informal growth patterns mean secondary cities are often data-poor and under-resourced, impacting the ability of governments to target development efforts, respond to emergencies, and design sustainable futures. The United Nations’ Sustainable Development Goal (SDG) 11 focuses on inclusive, safe, resilient, and sustainable cities and human settlements. SDG Indicator (SDGI) 11.3.1 calculates the ratio of land consumption rate to population growth rate to enhance inclusive and sustainable urbanization. Our paper compares three cities—Denpasar, Indonesia; Kharkiv, Ukraine; and Mekelle, Ethiopia—that were part of the Secondary Cities (2C) Initiative of the U.S. Department of State, Office of the Geographer and Global Issues to assess SDGI 11.3.1. The 2C Initiative focused on field-based participatory mapping for data generation to assist city planning. Urban form and population data are critical for calculating and visually representing this ratio. We examine the spatial extent of each city to assess land use efficiency (LUE) and track changes in urban form over time. With limited demographic and spatial data for secondary cities, we speculate whether SDGI 11.3.1 is useful for small- and medium-sized cities.

scholarly journals Precision Agriculture Workflow, from Data Collection to Data Management Using FOSS Tools: An Application in Northern Italy Vineyard

2021 ◽  
Vol 10 (4) ◽  
pp. 236
Author(s):  
Elena Belcore ◽  
Stefano Angeli ◽  
Elisabetta Colucci ◽  
Maria Angela Musci ◽  
Irene Aicardi
Keyword(s):  
Data Structure ◽  
Optical Sensors ◽  
Spatial Data ◽  
Precision Agriculture ◽  
New Technologies ◽  
Software Tools ◽  
Northern Italy ◽  
Data Generation ◽  
Individual Tree ◽  
Data Infrastructure

In the past decades, technology-based agriculture, also known as Precision Agriculture (PA) or smart farming, has grown, developing new technologies and innovative tools to manage data for the whole agricultural processes. In this framework, geographic information, and spatial data and tools such as UAVs (Unmanned Aerial Vehicles) and multispectral optical sensors play a crucial role in the geomatics as support techniques. PA needs software to store and process spatial data and the Free and Open Software System (FOSS) community kept pace with PA’s needs: several FOSS software tools have been developed for data gathering, analysis, and restitution. The adoption of FOSS solutions, WebGIS platforms, open databases, and spatial data infrastructure to process and store spatial and nonspatial acquired data helps to share information among different actors with user-friendly solutions. Nevertheless, a comprehensive open-source platform that, besides processing UAV data, allows directly storing, visualising, sharing, and querying the final results and the related information does not exist. Indeed, today, the PA’s data elaboration and management with a FOSS approach still require several different software tools. Moreover, although some commercial solutions presented platforms to support management in PA activities, none of these present a complete workflow including data from acquisition phase to processed and stored information. In this scenario, the paper aims to provide UAV and PA users with a FOSS-replicable methodology that can fit farming activities’ operational and management needs. Therefore, this work focuses on developing a totally FOSS workflow to visualise, process, analyse, and manage PA data. In detail, a multidisciplinary approach is adopted for creating an operative web-sharing tool able to manage Very High Resolution (VHR) agricultural multispectral-derived information gathered by UAV systems. A vineyard in Northern Italy is used as an example to show the workflow of data generation and the data structure of the web tool. A UAV survey was carried out using a six-band multispectral camera and the data were elaborated through the Structure from Motion (SfM) technique, resulting in 3 cm resolution orthophoto. A supervised classifier identified the phenological stage of under-row weeds and the rows with a 95% overall accuracy. Then, a set of GIS-developed algorithms allowed Individual Tree Detection (ITD) and spectral indices for monitoring the plant-based phytosanitary conditions. A spatial data structure was implemented to gather the data at canopy scale. The last step of the workflow concerned publishing data in an interactive 3D webGIS, allowing users to update the spatial database. The webGIS can be operated from web browsers and desktop GIS. The final result is a shared open platform obtained with nonproprietary software that can store data of different sources and scales.

scholarly journals Mapping Wildfires Using Sentinel 2 MSI and Landsat 8 Imagery: Spatial Data Generation for Forestry

2021 ◽  
Author(s):  
Sercan GÜLCİ ◽  
Kıvanç YÜKSEL ◽  
Selçuk GÜMÜŞ ◽  
Michael WİNG
Keyword(s):  
Spatial Data ◽  
Landsat 8 ◽  
Data Generation ◽  
Sentinel 2

A Two-Tier Spatial Index for Non-flat Spatial Data Broadcasting on Air

2014 ◽  
Vol E97.B (12) ◽  
pp. 2809-2818 ◽  
Author(s):  
SeokJin IM ◽  
HeeJoung HWANG
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Data Broadcasting

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

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