Showing papers in "Geo-spatial Information Science in 2017"
TL;DR: The role that GEO could play in enabling actual use of EO in support of the 2030 Agenda by directly addressing the Strategic Development Goal 17 on partnerships is focused on.
Abstract: This paper reviews the key role that Earth Observations (EO) play in achieving the Sustainable Development Goals (SDGs) as articulated in the 2030 Agenda document and in monitoring, measuring, and ...
244 citations
TL;DR: An understanding is provided toward the use of remote sensing and its applications to oil palm plantation monitoring and the existing knowledge gaps are identified and recommendations for further research are given.
Abstract: Oil palm becomes an increasingly important source of vegetable oil for its production exceeds soybean, sunflower, and rapeseed. The growth of the oil palm industry causes degradation to the environment, especially when the expansion of plantations goes uncontrolled. Remote sensing is a useful tool to monitor the development of oil palm plantations. In order to promote the use of remote sensing in the oil palm industry to support their drive for sustainability, this paper provides an understanding toward the use of remote sensing and its applications to oil palm plantation monitoring. In addition, the existing knowledge gaps are identified and recommendations for further research are given.
133 citations
TL;DR: A new strategic framework for linking a global policy to national geospatial capabilities is introduced and discussed, specifically in contributing their data resources toward measuring and monitoring the 17 Sustainable Development Goals, and their 169 associated targets, through the global indicator framework that anchors the 2030 Agenda for Sustainable Development.
Abstract: The 2030 Agenda for Sustainable Development provides a new global policy to guide the way countries collectively manage and transform the social, economic, and environmental dimensions of people and the planet over the next 15 years. Achieving sustainable development presents all countries and the global policy community with a set of significant development challenges that are almost entirely geographic in nature. Many of the issues impacting sustainable development can be analyzed, modeled, and mapped within a geographic context, which in turn can provide the integrative framework necessary for global collaboration, consensus and evidence-based decision-making. However, and despite significant advances in geospatial information technologies, there is a lack of awareness, understanding and uptake, particular at the policy and decision-making level, of the vital and integrative role of geospatial information and related enabling architectures such as National Spatial Data Infrastructures. This pap...
97 citations
TL;DR: A robust regional land-use mapping approach was developed by integrating OpenStreetMap data with the earth observation remote sensing imagery, which incorporates a vital temporal component to large-scale land- use mapping while effectively eliminating the typically burdensome computation and time/money demands of such work.
Abstract: A land-use map at the regional scale is a heavy computation task yet is critical to most landowners, researchers, and decision-makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating land classification maps at the regional scale: the necessity of large data-sets of training points and the expensive computation cost in terms of both money and time. Volunteered Geographic Information opens a new era in mapping and visualizing the physical world by providing an open-access database valuable georeferenced information collected by volunteer citizens. As one of the most well-known VGI initiatives, OpenStreetMap (OSM), contributes not only to road network distribution information but also to the potential for using these data to justify and delineate land patterns. Whereas, most large-scale mapping approaches – including regional and national scales – confuse “land cover” and “land-use”, or build up the land-use database based on modeled la...
52 citations
TL;DR: This work identifies important limitations and opportunities for using volunteered photographs as follows: the false precision of a photograph’s location is less useful for identifying on-the-spot land cover than the information it can give on neighbouring combinations of land cover.
Abstract: This paper extends recent research into the usefulness of volunteered photos for land cover extraction, and investigates whether this usefulness can be automatically assessed by an easily accessible, off-the-shelf neural network pre-trained on a variety of scene characteristics. Geo-tagged photographs are sometimes presented to volunteers as part of a game which requires them to extract relevant facts about land use. The challenge is to select the most relevant photographs in order to most efficiently extract the useful information while maintaining the engagement and interests of volunteers. By repurposing an existing network which had been trained on an extensive library of potentially relevant features, we can quickly carry out initial assessments of the general value of this approach, pick out especially salient features, and identify focus areas for future neural network training and development. We compare two approaches to extract land cover information from the network: a simple post hoc weighting approach accessible to non-technical audiences and a more complex decision tree approach that involves training on domain-specific features of interest. Both approaches had reasonable success in characterizing human influence within a scene when identifying the land use types (as classified by Urban Atlas) present within a buffer around the photograph’s location. This work identifies important limitations and opportunities for using volunteered photographs as follows: (1) the false precision of a photograph’s location is less useful for identifying on-the-spot land cover than the information it can give on neighbouring combinations of land cover; (2) ground-acquired photographs, interpreted by a neural network, can supplement plan view imagery by identifying features which will never be discernible from above; (3) when dealing with contexts where there are very few exemplars of particular classes, an independent a posteriori weighting of existing scene attributes and categories can buffer against over-specificity.
49 citations
TL;DR: India has a very robust remote sensing program that the Indian Remote Sensing Satellite (IRS) series of satellites were effectively used to monitor coastal habitats, landforms, shoreline, water quality, etc., and changes were identified during the last 40 years.
Abstract: The coastal zone is a region where land, ocean and atmosphere interact and hence it is dynamic in nature. India has a long coastline which was not adequately monitored until the advent of the satellite remote sensing era in the 70s. India has a very robust remote sensing program that the Indian Remote Sensing Satellite (IRS) series of satellites were effectively used to monitor coastal habitats, landforms, shoreline, water quality, etc., and changes were identified during the last 40 years. The classification system for coastal habitats and the classification and geometric accuracies of products were standardized. Detailed information for mangroves communities and characteristics of coral reefs were generated. The high and low tide lines were delineated seamlessly for the entire coastline using satellite data. All these data were organized in a GIS and the coastal database for the entire country was created. Impacts of various hazards on such as cyclones, tsunami and sea level changes on coastal h...
46 citations
TL;DR: Results show that black and Hispanic neighborhoods are disadvantaged when it comes to crowd-sourced data coverage, that PokéStops occur more frequently in commercial, recreational and touristic sites and around universities, and thatPokéStops tend to cluster around gyms.
Abstract: In 2016, Niantic Labs released Pokemon Go, an augmented reality smartphone game that attracted millions of users worldwide. This game allows users to “catch” Pokemons through their mobile cameras in different geographic locations that often correspond to prominent places. This paper analyzes the distribution of PokeStops, Pokemon gyms, and spawnpoints in selected urban areas of South Florida and Boston. It identifies which socioeconomic variables and land-use categories affect the density of PokeStops, and how PokeStops and gyms cluster relative to each other. Using nearest neighbor analysis, this paper assesses also how actual PokeStop locations are reflected in Yelp’s “PokeStop nearby” attribute. Results show that black and Hispanic neighborhoods are disadvantaged when it comes to crowd-sourced data coverage, that PokeStops occur more frequently in commercial, recreational and touristic sites and around universities, and that PokeStops tend to cluster around gyms. The latter suggests that these ...
45 citations
TL;DR: The concept and model of the Earth Observation Brain − the intelligent earth system based on events perception in this paper is proposed and some key technologies needed to be solved in the EOB are described.
Abstract: Since the twenty-first century, with the rapid development of high-resolution earth observation satellites, the earth observation satellite system has developed from the initial single satellite observation model to the current satellite constellation formed by light and small satellites observation model. All-weather and all-directional fine earth observation can now be realized. In the future, the satellite constellation, communication satellites, navigation satellites, and aircrafts are linked through dynamic linking network to form an air-space information network to realize real-time services of intelligent air-space information. To further enhance the perception, cognition, and quick response ability of the network, we propose the concept and model of the Earth Observation Brain (EOB) − the intelligent earth system based on events perception in this paper. Then, some key technologies needed to be solved in the EOB are also described. An application example is illustrated to show the process ...
37 citations
TL;DR: This article examines the history of the OSM folksonomy, with the aim to predict its future evolution, and investigates the historical and future scope and granularity of the folksonomy.
Abstract: The comprehension of folksonomies is of high importance when making sense of Volunteered Geographic Information (VGI), in particular in the case of OpenStreetMap (OSM). So far, only little research has been conducted to understand the role and the evolution of folksonomies in VGI and OSM, which is despite the fact that without a comprehension of the folksonomies the thematic dimension of data can hardly be used. This article examines the history of the OSM folksonomy, with the aim to predict its future evolution. In particular, we explore how the documentation of the OSM folksonomy relates to its actual use in the data, and we investigate the historical and future scope and granularity of the folksonomy. Finally, a visualization technique is proposed to examine the folksonomy in more detail.
36 citations
TL;DR: A novel nonlinear feature extraction method for hyperspectral images that explores the use of image segmentation, which benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification.
Abstract: Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.
33 citations
TL;DR: This research examines tweeting activity during two earthquakes in Italy and Myanmar, and compares the granularity of geographic references used, user profile characteristics that are related to credibility, and the performance of Naïve Bayes models for classifying Tweets when used on data from a different region than the one used to train the model.
Abstract: Twitter is a well-known microblogging platform for rapid diffusion of views, ideas, and information. During disasters, it has widely been used to communicate evacuation plans, distribute calls for help, and assist in damage assessment. The reliability of such information is very important for decision-making in a crisis situation, but also difficult to assess. There is little research so far on the transferability of quality assessment methods from one geographic region to another. The main contribution of this research is to study Twitter usage characteristics of users based in different geographic locations during disasters. We examine tweeting activity during two earthquakes in Italy and Myanmar. We compare the granularity of geographic references used, user profile characteristics that are related to credibility, and the performance of Naive Bayes models for classifying Tweets when used on data from a different region than the one used to train the model. Our results show similar geographic gr...
TL;DR: It is found that total observation numbers are a good estimator of species completeness of citizen science data from US NPs and there is also a close relationship betweenspecies completeness and the ratio of new species added to observation data vs. observation numbers in a given year.
Abstract: Observations of living organisms by citizen scientists that are reported to online portals are a valuable source of information. They are also a special kind of volunteered geographic information (VGI). VGI data have issues of completeness, which arise from biases caused by the opportunistic nature of the data collection process. We examined the completeness of bird species represented in citizen science observation data from eBird and iNaturalist in US National Parks (NPs). We used approaches for completeness estimation which were developed for data from OpenStreetMap, a crowdsourced map of the world. First, we used an extrinsic approach, comparing species lists from citizen science data with National Park Service lists. Second, we examined two intrinsic approaches using total observation numbers in NPs and the development of the number of new species being added to the data-set over time. Results from the extrinsic approach provided appropriate completeness estimations to evaluate the intrinsic ...
TL;DR: A machine learning-based data integration approach for improving global-scale forest cover characterization and showed that other major vegetative land cover types such as cropland, woodland, grassland, and wetland all exhibit similar magnitude of discrepancies as forest among existing land cover maps.
Abstract: Global-scale land cover characterization has advanced from a spatial resolution of 1 × 1° in the mid-1990s to 30 × 30 m resolution to date. However, some mapping challenges exist persistently regardless of the increasing spatial resolution. Data fusion has been proved as an effective way of improving land cover characterization. Here we applied a machine learning-based data integration approach for improving global-scale forest cover characterization. The approach employed six coarse-resolution (250–1000 m) global land cover maps as input and various regional, higher-resolution land cover data-sets as reference to build regression tree models per continent. The average error of 10-fold cross validation of the regression tree models varied between 7.70 and 15.68% forest cover and the r2 varied between 0.76 and 0.94, indicating the robustness of the trained models. As a result of data fusion, the synthesized global forest cover map was more accurate than any input global product. We also showed that...
TL;DR: Recent achievements on accelerating the process of implementing the recommendations of the INSPIRE Directive in Bulgaria are outlined and comparative analyses of the main indicators for metadata, data-sets, and data services provided by EU member states are discussed.
Abstract: The paper aims to present a concise overview of the current status of the national spatial data infrastructures (SDI) of the European Union (EU) member states combined with specific peculiarities for Bulgaria. Some major challenges within the progress of the EU SDIs establishing, which is regulated by the European Directive INSPIRE (Infrastructure for spatial information in Europe) toward establishment of a SDI for environmental policies and activities, are marked out. Available comparative analyses of the main indicators for metadata, data-sets, and data services provided by EU member states are briefly discussed as a special attention is given to the Bulgarian progress. Recent achievements on accelerating the process of implementing the recommendations of the INSPIRE Directive in Bulgaria are outlined.
TL;DR: The salient features of this work are the preservation of actual size and shape of the shorelines, run-time structuring element definition, semi-automation, faster processing, and single band adaptability.
Abstract: Shoreline extraction is fundamental and inevitable for several studies. Ascertaining the precise spatial location of the shoreline is crucial. Recently, the need for using remote sensing data to ac...
TL;DR: This paper analyzes the myriad forces that are driving perceptality and the future of geospatial intelligence and presents real-world implications and examples of its industrial application.
Abstract: For centuries, humans’ capacity to capture and depict physical space has played a central role in industrial and societal development. However, the digital revolution and the emergence of networked devices and services accelerate geospatial capture, coordination, and intelligence in unprecedented ways. Underlying the digital transformation of industry and society is the fusion of the physical and digital worlds – ‘perceptality’ – where geospatial perception and reality merge. This paper analyzes the myriad forces that are driving perceptality and the future of geospatial intelligence and presents real-world implications and examples of its industrial application. Applications of sensors, robotics, cameras, machine learning, encryption, cloud computing and other software, and hardware intelligence are converging, enabling new ways for organizations and their equipment to perceive and capture reality. Meanwhile, demands for performance, reliability, and security are pushing compute ‘to the edge’ whe...
TL;DR: To verify the effectiveness of the presented classification framework and to confirm the LWIR’s complementary role in the urban mapping task, experiment results are evaluated by the grss_dfc_2014 data-set.
Abstract: Multi-sensor and multi-resolution source images consisting of optical and long-wave infrared (LWIR) images are analyzed separately and then combined for urban mapping in this study. The framework of its methodology is based on a two-level classification approach. In the first level, contributions of these two data sources in urban mapping are examined extensively by four types of classifications, i.e. spectral-based, spectral-spatial-based, joint classification, and multiple feature classification. In the second level, an objected-based approach is applied to decline the boundaries. The specificity of our proposed framework not only lies in the combination of two different images, but also the exploration of the LWIR image as one complementary spectral information for urban mapping. To verify the effectiveness of the presented classification framework and to confirm the LWIR’s complementary role in the urban mapping task, experiment results are evaluated by the grss_dfc_2014 data-set.
TL;DR: This paper reviews educational activities from the ISPRS perspective and summarizes lessons learned from these actions and a vision towards future trends of Geoinformatics education in theISPRS is provided.
Abstract: Geoinformatics education is a key factor for sustainable development of geo-spatial sciences and industries. There have been a variety of educational activities focusing on education and training, technology transfer, and capability building in photogrammetry, remote sensing, and spatial information science, together known as Geoinformatics. Geoinformatics education is an essential mission and even determinant in the ISPRS society. The paper discusses key issues in Geoinformatics education. It reviews educational activities from the ISPRS perspective and summarizes lessons learned from these actions. A vision towards future trends of Geoinformatics education in the ISPRS is provided.
TL;DR: This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, and the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012.
Abstract: Urban areas are of paramount significance to both the individuals and communities at local and regional scales. However, the rapid growth of urban areas exerts effects on climate, biodiversity, hydrology, and natural ecosystems worldwide. Therefore, regular and up-to-date information related to urban extent is necessary to monitor the impacts of urban areas at local, regional, and potentially global scales. This study presents a new urban map of Eurasia at 500 m resolution using multi-source geospatial data, including Moderate Resolution Imaging Spectroradiometer (MODIS) data of 2013, population density of 2012, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) nighttime lights of 2012, and constructed Impervious Surface Area (ISA) data of 2010. The Eurasian urban map was created using the threshold method for these data, combined with references of fine resolution Landsat and Google Earth imagery. The resultant map was compared with nine global urban maps and w...
TL;DR: In this study, Hölder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture and a threshold is derived to segment the transformed image into textured and non-textured regions.
Abstract: Pixel-based or texture-based classification technique individually does not yield an appropriate result in classifying the high spatial resolution remote sensing imagery since it comprises textured and non-textured regions. In this study, Holder exponents (HE) and variance (VAR) are used together to transform the image for measuring texture. A threshold is derived to segment the transformed image into textured and non-textured regions. Subsequently, the original image is extracted into textured and non-textured regions using this segmented image mask. Afterward, extracted textured region is classified using ISODATA classification algorithm considering HE, VAR, and intensity values of individual pixel of textured region. And extracted non-textured region of the image is classified using ISODATA classification algorithm. In case of non-textured region, HE and VAR value of individual pixel is not considered for classification for significant textural variation is not found among different classes. Co...
TL;DR: The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area and indicates that there is a good agreement between output SM and SM of ground truth for agricultural area.
Abstract: Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from sat...
TL;DR: The authors examines the current state of geospatial science in Australia: positioning, earth observation (EO), and spatial infrastructures, and discusses the limit at which these three areas can be combined.
Abstract: This paper examines the current state of three of the key areas of geospatial science in Australia: positioning; earth observation (EO); and spatial infrastructures. The paper discusses the limitat...
TL;DR: The study proposes planning-relevant GIS curricula to improve the level of GIS use in planning practice and indicates that considerable progress has been made in the application of geospatial information in municipal planning; however, there are impediments limiting the full utilization of geosphere information in local municipalities.
Abstract: Since the introduction of geographic information systems (GIS) in the 1960s, it has evolved tremendously to an extent that it permeates our daily lives. Initially, GIS usage started in the develope...
TL;DR: The new frontiers regarding the efficient handling of big geospatial data, supporting and sharing of crowd sourced/citizen science data, integration with semantic heterogeneity, and inclusion of agile processes for continuous improvement of geosp spatial technology are discussed.
Abstract: The process of sharing of data has become easier than ever with the advancement of cloud computing and software tools. However, big challenges remain such as efficient handling of big geospatial data, supporting and sharing of crowd sourced/citizen science data, integration with semantic heterogeneity, and inclusion of agile processes for continuous improvement of geospatial technology. This paper discusses the new frontiers regarding these challenges and the related work performed by the Open Geospatial Consortium, the world leading organization focused on developing open geospatial standards that “geo-enable” the Web, wireless, and location-based services and mainstream IT.
TL;DR: To identify the nature of events that might have facilitated or hindered enrollments in the OpenStreetMap (OSM) project over time, the correlations between the number of new participants and the events that dotted its history were analyzed.
Abstract: The number of people registering in an online community depends on two main factors: interest in, and awareness of, the project. Registering to a project does not, however, imply contributing to it, as lacking the knowledge and skills can be a barrier to participation. In order to identify the nature of events that might have facilitated or hindered enrollments in the OpenStreetMap (OSM) project over time, we analyzed the correlations between the number of new participants and the events that dotted its history. Four different metrics were defined to characterize participants’ behaviors: the daily number of registrations, the daily number of participants that made a first contribution, the delays between contributors’ registration and their first edits, and a daily contribution ratio built from the number of new contributors and the number of new registered members. Time series analyses were used to identify trends, and outstanding variations of the number of participants. An inventory of events t...
TL;DR: A method for intersecting vehicle trajectories and extracting their intersection points for selected rush hours in urban environments is introduced and the usefulness of polygons representing frequently visited locations for analyses in urban planning and traffic engineering is reasoned out.
Abstract: Detecting and describing movement of vehicles in established transportation infrastructures is an important task. It helps to predict periodical traffic patterns for optimizing traffic regulations ...
TL;DR: In this paper, the impact of Brown Carbon (BrC) to aerosol light absorption has been paid more attention recently and there are a large number of studies showing that the influence of BrC on radiative forcing should not be ignored.
Abstract: The impact of Brown Carbon (BrC) to aerosol light absorption has been paid more attention recently and there are a large number of studies showing that the influence of BrC on radiative forcing should not be ignored. BrC also acts as an important component of haze pollution which is occurring frequently in Wuhan, China. Therefore, it is essential to estimate their optical properties, composition, and mass concentration. Considering most haze pollution happens during the coldest time, we retrieved BrC columnar content during winter in Wuhan for the first time. Our method bases on the fact that BrC showed the strong spectral dependence on UV-light absorption. Using this method, we found that BrC makes up the small proportions of total aerosol volume (less than 10%). In the winter of 2011, we retrieved the daily-averaged columnar-integrated mass concentration of BrC on clear day is 4.353 mg/m2 while that of haze day is 12.750 mg/m2. According to the sensitivity study, we found that the results highly...
TL;DR: This paper describes and discusses this new geospatial data service model, which allows the user to directly access multi-source satellite data, manage the data order, and carry out automatic massive data production and delivery in the 21AT TripleSat Constellation constellation.
Abstract: With the increase of different sensors, applications and customers, the demand from data providers and users is for a new geospatial data service model, which supports low cost, high dexterity, and which would provide a comprehensive service. Based on such requirements and demands, the 21AT TripleSat constellation terminal and data delivery and management system has been developed by a Beijing based high-tech enterprise, Twenty First Century Aerospace Technology Co., Ltd. (21AT). The company is the first commercial Earth observation satellite operator and service provider in China. This new geospatial data service model allows the user to directly access multi-source satellite data, manage the data order, and carry out automatic massive data production and delivery. The solution also implements safe and hierarchical user management, statistical data analysis, and automatic information reports. In addition, a mobile application is also available for users to easily access system functions. This new...
TL;DR: This study investigates how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information, and demonstrates the efficiency of the fusion approach, with significant improvements over some conventional methods.
Abstract: Close-range hyperspectral images are a promising source of information in plant biology, in particular, for in vivo study of physiological changes. In this study, we investigate how data fusion can improve the detection of leaf elements by combining pixel reflectance and morphological information. The detection of image regions associated to the leaf structures is the first step toward quantitative analysis on the physical effects that genetic manipulation, disease infections, and environmental conditions have in plants. We tested our fusion approach on Musa acuminata (banana) leaf images and compared its discriminant capability to similar techniques used in remote sensing. Experimental results demonstrate the efficiency of our fusion approach, with significant improvements over some conventional methods.