Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-28-2022
Lucas Casuccio, André Kotze
Abstract. Uniform plant spacing along crop rows is a primary concern in maximising yield in precision agriculture, and research has shown that variation in this spacing uniformity has a detrimental effect on productive potential. This irregularity needs to be evaluated as early and efficiently as possible to facilitate effective decision-making. Traditionally, variation in seedling spacing is sampled manually on site, however recent technological developments have made it possible to refine, scale and automate this process. Using machine-learning (ML) object detection techniques, plants can be detected in very high-resolution RGB (redgreen-blue) imagery acquired by an unmanned aerial vehicle (UAV), and after processing and geometric analysis of the results a measurement of the variability in intra-row plant distances can be obtained. This proposed technique is superior to traditional methods since the sampling can be made over more area in less time, and the results are more representative and objective. The main benefits are speed, accuracy and cost reduction. This work aims to demonstrate the feasibility of automatically assessing sowing quality in any number of images, using ML object detection and the Shapely Python library for geometrical analysis. The prototype model can detect 99.35% of corn plants in test data from the same field, but also detects 1.89% false positives. Our geometric analysis algorithm has been shown to be robust in finding planting rows orientation and interplant lines in test cases. The result is a coefficient of variation (CV) calculated per sample image, which can be visualised geographically to support decision-making.
{"title":"Corn planting quality assessment in very high-resolution RGB UAV imagery using Yolov5 and Python","authors":"Lucas Casuccio, André Kotze","doi":"10.5194/agile-giss-3-28-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-28-2022","url":null,"abstract":"Abstract. Uniform plant spacing along crop rows is a primary concern in maximising yield in precision agriculture, and research has shown that variation in this spacing uniformity has a detrimental effect on productive potential. This irregularity needs to be evaluated as early and efficiently as possible to facilitate effective decision-making. Traditionally, variation in seedling spacing is sampled manually on site, however recent technological developments have made it possible to refine, scale and automate this process. Using machine-learning (ML) object detection techniques, plants can be detected in very high-resolution RGB (redgreen-blue) imagery acquired by an unmanned aerial vehicle (UAV), and after processing and geometric analysis of the results a measurement of the variability in intra-row plant distances can be obtained. This proposed technique is superior to traditional methods since the sampling can be made over more area in less time, and the results are more representative and objective. The main benefits are speed, accuracy and cost reduction. This work aims to demonstrate the feasibility of automatically assessing sowing quality in any number of images, using ML object detection and the Shapely Python library for geometrical analysis. The prototype model can detect 99.35% of corn plants in test data from the same field, but also detects 1.89% false positives. Our geometric analysis algorithm has been shown to be robust in finding planting rows orientation and interplant lines in test cases. The result is a coefficient of variation (CV) calculated per sample image, which can be visualised geographically to support decision-making.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114832533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-39-2022
Stefan S. Ivanovic, Ross M Purves
Abstract. This paper aims to contribute to a better understanding of the interactions between traffic perturbations and bike sharing use. More specifically we propose a framework for comparative spatial temporal analyses of public transport strikes and massive protests effects on bike sharing program in Paris. We find opposite effects on bike sharing demand due to public transport strikes and protests. The former causes a considerable rise in bike sharing demand particularly during the daily rush hours, while the latter precipitates a drop of activity constantly during the protest day. Our approach allows tracing bike sharing demand changes induced by traffic perturbations on an hourly level.
{"title":"Effects of traffic perturbations on bike sharing demand – a case study of public transport strikes and protests in Paris","authors":"Stefan S. Ivanovic, Ross M Purves","doi":"10.5194/agile-giss-3-39-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-39-2022","url":null,"abstract":"Abstract. This paper aims to contribute to a better understanding of the interactions between traffic perturbations and bike sharing use. More specifically we propose a framework for comparative spatial temporal analyses of public transport strikes and massive protests effects on bike sharing program in Paris. We find opposite effects on bike sharing demand due to public transport strikes and protests. The former causes a considerable rise in bike sharing demand particularly during the daily rush hours, while the latter precipitates a drop of activity constantly during the protest day. Our approach allows tracing bike sharing demand changes induced by traffic perturbations on an hourly level.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124373099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-30-2022
A. Comber, Paul Harris, Kristina Bratkova, H. H. Phe, M. Kieu, Quang Thanh Bui, Thi-Thuy-Nghiem Nguyen, Eric Wanjau, N. Malleson
Abstract. The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data.
{"title":"Handling the MAUP: methods for identifying appropriate scales of aggregation based on measures on spatial and non-spatial variance","authors":"A. Comber, Paul Harris, Kristina Bratkova, H. H. Phe, M. Kieu, Quang Thanh Bui, Thi-Thuy-Nghiem Nguyen, Eric Wanjau, N. Malleson","doi":"10.5194/agile-giss-3-30-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-30-2022","url":null,"abstract":"Abstract. The Modifiable Areal Unit Problem or MAUP is frequently alluded to but rarely addressed directly. The MAUP posits that statistical distributions, relationships and trends can exhibit very different properties when the same data are aggregated or combined over different reporting units or scales. This paper explores a number of approaches for determining appropriate scales of spatial aggregation. It examines a travel survey, undertaken in Ha Noi, Vietnam, that captures attitudes towards a potential ban of motorised transport in the city centre. The data are rich, capturing travel destinations, purposes, modes and frequencies, as well as respondent demographics (age, occupation, housing etc) including home locations. The dataset is highly dimensional, with a large n (26339 records) and a large m (142 fields). When the raw individual level data are used to analyse the factors associated with travel ban attitudes, the resultant models are weak and inconclusive - the data are too noisy. Aggregating the data can overcome this, but this raises the question of appropriate aggregation scales. This paper demonstrates how aggregation scales can be evaluated using a range of different metrics related to spatial and non-spatial variances. In so doing it demonstrates how the MAUP can be directly addressed in analyses of spatial data.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"418 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116683480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-21-2022
Rui Zhu, K. Janowicz, Gengchen Mai, Ling Cai, Meilin Shi
Abstract. The longer the COVID-19 pandemic lasts, the more apparent it becomes that understanding its social drivers may be as important as understanding the virus itself. One such social driver is misinformation and distrust in institutions. This is particularly interesting as the scientific process is more transparent than ever before. Numerous scientific teams have published datasets that cover almost any imaginable aspects of COVID-19 during the last two years. However, consistently and efficiently integrating and making sense of these separate data “silos” to scientists, decision makers, journalists, and more importantly the general public remain a key challenge with important implications for transparency. Several types of knowledge graphs have been published to tackle this issue and to enable data crosswalks by providing rich contextual information. Interestingly, none of these graphs has focused on COVID-19 forecasts despite them acting as the underpinning for decision making. In this work we motivate the need for exposing forecasts as a knowledge graph, showcase queries that run against the graph, and geographically interlink forecasts with indicators of economic impacts.
{"title":"COVID-Forecast-Graph: An Open Knowledge Graph for Consolidating COVID-19 Forecasts and Economic Indicators via Place and Time","authors":"Rui Zhu, K. Janowicz, Gengchen Mai, Ling Cai, Meilin Shi","doi":"10.5194/agile-giss-3-21-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-21-2022","url":null,"abstract":"Abstract. The longer the COVID-19 pandemic lasts, the more apparent it becomes that understanding its social drivers may be as important as understanding the virus itself. One such social driver is misinformation and distrust in institutions. This is particularly interesting as the scientific process is more transparent than ever before. Numerous scientific teams have published datasets that cover almost any imaginable aspects of COVID-19 during the last two years. However, consistently and efficiently integrating and making sense of these separate data “silos” to scientists, decision makers, journalists, and more importantly the general public remain a key challenge with important implications for transparency. Several types of knowledge graphs have been published to tackle this issue and to enable data crosswalks by providing rich contextual information. Interestingly, none of these graphs has focused on COVID-19 forecasts despite them acting as the underpinning for decision making. In this work we motivate the need for exposing forecasts as a knowledge graph, showcase queries that run against the graph, and geographically interlink forecasts with indicators of economic impacts.","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-3-2022
Haojun Cai, Yanan Xin, Henry Martin, M. Raubal
Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.
{"title":"Optimizing Electric Vehicle Charging Schedules Based on Probabilistic Forecast of Individual Mobility","authors":"Haojun Cai, Yanan Xin, Henry Martin, M. Raubal","doi":"10.5194/agile-giss-3-3-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-3-2022","url":null,"abstract":"Abstract. The number of electric vehicles (EVs) has been rapidly increasing over the last decade, motivated by the effort to decrease greenhouse gas emissions and the fast development of battery technology. This trend challenges distribution grids since EVs will bring significant stress if the charging of many EVs is not coordinated. Among the many strategies to cope with this challenge, next-day EV energy demand forecasting plays a key role. Existing studies have focused on predicting the next-day energy demand of EVs on the aggregated and individual levels. However, these studies have not yet extensively considered individual user mobility behaviors, which exhibit a high level of predictability. In this study, we consider several mobility features of individual users when forecasting the next-day energy demand of individual EVs. Three types of quantile regression models are used to generate probabilistic forecasts of energy demand, particularly the next-day energy consumption and parking duration. Based on the prediction results, two time-shifting smart charging strategies are designed: unidirectional and bidirectional smart charging. These two strategies are compared with an uncontrolled charging baseline to evaluate their financial benefits and peak-shaving effects. Our results show that human mobility features can partially improve the prediction of next-day individual EV energy demand. Additionally, users and distribution grids can benefit from smart charging strategies both financially and technically.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130175453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-6-2022
Ditsuhi Iskandaryan, S. Di Sabatino, Francisco Ramos, S. Trilles
Abstract. Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach.
{"title":"Exploratory Analysis and Feature Selection for the Prediction of Nitrogen Dioxide","authors":"Ditsuhi Iskandaryan, S. Di Sabatino, Francisco Ramos, S. Trilles","doi":"10.5194/agile-giss-3-6-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-6-2022","url":null,"abstract":"Abstract. Nitrogen dioxide is one of the most hazardous pollutants identified by the World Health Organisation. Predicting and reducing pollutants is becoming a very urgent task and many methods have been used to predict their concentration, such as physical or machine learning models. In addition to choosing the right model, it is also critical to choose the appropriate features. This work focuses on the spatiotemporal prediction of nitrogen dioxide concentration using Bidirectional Convolutional LSTM integrated with the exploration of nitrogen dioxide and associated features, as well as the implementation of feature selection methods. The Root Mean Square Error and the Mean Absolute Error were used to evaluate the proposed approach.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133755170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-42-2022
Kevin Kocon, Michel Krämer, Hendrik M. Würz
Abstract. We present the results from evaluating various Convolutional Neural Network (CNN) models to compare their usefulness for forest type classification. Machine Learning based on CNNs is known to be suitable to identify relevant patterns in remote sensing imagery. With the availability of free data sets (e.g. the Copernicus Sentinel-2 data), Machine Learning can be utilized for forest monitoring, which provides useful and timely information helping to measure and counteract the effects of climate change. To this end, we performed a case study with publicly available data from the federal state of North Rhine-Westphalia in Germany. We created an automated pipeline to preprocess and filter this data and trained the CNN models UNet, PSPNet, SegNet, and FCN-8. Since the data contained large rural areas, we augmented the imagery to improve classification results. We reapplied the trained models to the data, compared the results for each model, and evaluated the effect of augmentation. Our results show that UNet performs best with a categorical accuracy of 73% when trained with augmented imagery.
{"title":"Comparison of CNN-based segmentation models for forest type classification","authors":"Kevin Kocon, Michel Krämer, Hendrik M. Würz","doi":"10.5194/agile-giss-3-42-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-42-2022","url":null,"abstract":"Abstract. We present the results from evaluating various Convolutional Neural Network (CNN) models to compare their usefulness for forest type classification. Machine Learning based on CNNs is known to be suitable to identify relevant patterns in remote sensing imagery. With the availability of free data sets (e.g. the Copernicus Sentinel-2 data), Machine Learning can be utilized for forest monitoring, which provides useful and timely information helping to measure and counteract the effects of climate change. To this end, we performed a case study with publicly available data from the federal state of North Rhine-Westphalia in Germany. We created an automated pipeline to preprocess and filter this data and trained the CNN models UNet, PSPNet, SegNet, and FCN-8. Since the data contained large rural areas, we augmented the imagery to improve classification results. We reapplied the trained models to the data, compared the results for each model, and evaluated the effect of augmentation. Our results show that UNet performs best with a categorical accuracy of 73% when trained with augmented imagery.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124856241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-27-2022
Sebastian T. Brinkmann, Dominik Kremer, B. B. Walker
Abstract. Studies from public and environmental health show strong indication of the importance of visible urban green space. However, current approaches for modelling viewshed based greenness visibility still have high computation costs. As a consequence, comparative studies of city-wide visible greenness, everyday mobility, and individual attention are still at the edge of feasibility. Known optimisations focus on reducing the computation time of single viewsheds. As point-based viewsheds are computed using geospatial data, current approaches seek to accelerate calculation using intelligent data structures or spatial indexes (at the cost of additional memory) or develop approximative heuristic solutions. In contrast, as we aim to process large numbers of viewsheds with fixed parameterisations, we use a prototyping approach preprocessing a single viewshed template to store common prefixes of consecutive lines of sight that can be applied to followup viewsheds by a simple offset operation. Our evaluation shows an average improvement of 34% over the benchmark algorithm (RFVS), with even stronger improvements for large viewsheds. We anticipate that these findings lay the groundwork for further optimisation of point-based viewshed computation, improving the feasibility of subsequent comparative studies in the field of public and environmental health.
{"title":"Modelling eye-level visibility of urban green space: Optimising city-wide point-based viewshed computations through prototyping","authors":"Sebastian T. Brinkmann, Dominik Kremer, B. B. Walker","doi":"10.5194/agile-giss-3-27-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-27-2022","url":null,"abstract":"Abstract. Studies from public and environmental health show strong indication of the importance of visible urban green space. However, current approaches for modelling viewshed based greenness visibility still have high computation costs. As a consequence, comparative studies of city-wide visible greenness, everyday mobility, and individual attention are still at the edge of feasibility. Known optimisations focus on reducing the computation time of single viewsheds. As point-based viewsheds are computed using geospatial data, current approaches seek to accelerate calculation using intelligent data structures or spatial indexes (at the cost of additional memory) or develop approximative heuristic solutions. In contrast, as we aim to process large numbers of viewsheds with fixed parameterisations, we use a prototyping approach preprocessing a single viewshed template to store common prefixes of consecutive lines of sight that can be applied to followup viewsheds by a simple offset operation. Our evaluation shows an average improvement of 34% over the benchmark algorithm (RFVS), with even stronger improvements for large viewsheds. We anticipate that these findings lay the groundwork for further optimisation of point-based viewshed computation, improving the feasibility of subsequent comparative studies in the field of public and environmental health.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116979775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-41-2022
A. Kmoch, O. Matsibora, I. Vasilyev, E. Uuemaa
Abstract. Discrete Global Grid Systems (DGGS) are spatial reference systems that use a hierarchical tessellation of cells to partition and address the globe and provide alternative spatial data format and indexing methods as compared to traditional vector and raster spatial data. In order to effectively use DGGS, functional software needs to be available and data needs to be indexed into a DGGS. We compare the software APIs of the 5 main open-source DGGS implementations – Uber H3, Google S2, rHEALPix by Landcare Research New Zealand, RiskAware OpenEAGGR, and DGGRID by Southern Oregon University – and present exemplary workflows for converting spatial and vector and raster datasets into DGGS-indexed format. We summarize, that Uber H3 and Google S2 provide more mature software library functionalities and DGGRID provides excellent functionality to construct grids with desired geometric properties and to load point data but does not provide functions for traversal and navigation within a grid after its construction.
{"title":"Applied open-source Discrete Global Grid Systems","authors":"A. Kmoch, O. Matsibora, I. Vasilyev, E. Uuemaa","doi":"10.5194/agile-giss-3-41-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-41-2022","url":null,"abstract":"Abstract. Discrete Global Grid Systems (DGGS) are spatial reference systems that use a hierarchical tessellation of cells to partition and address the globe and provide alternative spatial data format and indexing methods as compared to traditional vector and raster spatial data. In order to effectively use DGGS, functional software needs to be available and data needs to be indexed into a DGGS. We compare the software APIs of the 5 main open-source DGGS implementations – Uber H3, Google S2, rHEALPix by Landcare Research New Zealand, RiskAware OpenEAGGR, and DGGRID by Southern Oregon University – and present exemplary workflows for converting spatial and vector and raster datasets into DGGS-indexed format. We summarize, that Uber H3 and Google S2 provide more mature software library functionalities and DGGRID provides excellent functionality to construct grids with desired geometric properties and to load point data but does not provide functions for traversal and navigation within a grid after its construction.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123028068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-06-10DOI: 10.5194/agile-giss-3-13-2022
Yousef Qamaz, A. Schwering, Janina Bistron
Abstract. Although Global Positioning System (GPS) is widely used in outdoor location-based services, it still lacks precision due to obstacles that reduce its performance, such as near tall buildings, with bad weather conditions, and under tree canopies. In some situations, inaccurate localization or delay in getting location locks can adversely affect some location-based services’ functionality. Furthermore, it might make these services less efficient or even completely useless, especially when the receiver device has no SIM card or when the service requires a precision higher than three meters. As a solution to this issue, this study designs, develops, and evaluates a prototype location-based system that uses Bluetooth Low Energy beacons for short-range positioning in outdoor environments as a GPS alternative. The proposed system is a game that includes navigational tasks, which can be accomplished by reaching the proximity of two meters from the beacon’s location. The study involved conducting an experiment outdoors with a focus on areas where GPS signals are degraded to assess our proposed system’s efficiency and feasibility compared to the usage of GPS. The results proved BLE beacons’ ability to provide better positioning results than GPS, not only in terms of accuracy but also in terms of stability of positioning results over time. Based on the findings, the study outlines a set of guidelines to be considered in choosing a suitable positioning technology.
{"title":"Experimental evaluation of using BLE beacon for outdoor positioning in GPS-denied environment","authors":"Yousef Qamaz, A. Schwering, Janina Bistron","doi":"10.5194/agile-giss-3-13-2022","DOIUrl":"https://doi.org/10.5194/agile-giss-3-13-2022","url":null,"abstract":"Abstract. Although Global Positioning System (GPS) is widely used in outdoor location-based services, it still lacks precision due to obstacles that reduce its performance, such as near tall buildings, with bad weather conditions, and under tree canopies. In some situations, inaccurate localization or delay in getting location locks can adversely affect some location-based services’ functionality. Furthermore, it might make these services less efficient or even completely useless, especially when the receiver device has no SIM card or when the service requires a precision higher than three meters. As a solution to this issue, this study designs, develops, and evaluates a prototype location-based system that uses Bluetooth Low Energy beacons for short-range positioning in outdoor environments as a GPS alternative. The proposed system is a game that includes navigational tasks, which can be accomplished by reaching the proximity of two meters from the beacon’s location. The study involved conducting an experiment outdoors with a focus on areas where GPS signals are degraded to assess our proposed system’s efficiency and feasibility compared to the usage of GPS. The results proved BLE beacons’ ability to provide better positioning results than GPS, not only in terms of accuracy but also in terms of stability of positioning results over time. Based on the findings, the study outlines a set of guidelines to be considered in choosing a suitable positioning technology.\u0000","PeriodicalId":116168,"journal":{"name":"AGILE: GIScience Series","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123549063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：