Hasil untuk "Mathematical geography. Cartography"

Shoukai Sun, Anke Huss, Derek Karssenberg et al.

Urban greenery, as a critical urban landscape component, plays an important role in improving the living environments’ and residents’ well-being. Previous studies have predominantly adopted satellite image-based vegetation measurements. This study aims to quantify pedestrian-perspective greenery visibility using Google Street View (GSV) images and to understand how greenery types and built environment characteristics influence the correlation between pedestrian and aerial greenery assessments. We collected GSV images located on 34,601 sampling points and applied the DeepLab v3+ deep learning model to quantify green view index (GVI) from the pedestrian perspective. We distinguished green vegetation view index (GVVI) and green terrain view index (GTVI) to differentiate vertical and horizontal greenery types. Normalized difference vegetation index (NDVI) was extracted from Sentinel-2 images using circular buffers of varying radii (10–200 m) centered on GSV sampling points. Sampling points were filtered based on the buffer distance to avoid overlapping NDVI pixels in neighboring sampling points. Spearman correlation analysis was conducted across different typologies (urban, intermediate, rural) to examine GVI-NDVI relationships. Street-level greenery exhibited substantial spatial heterogeneity across the whole of the study area (Basel, Switzerland). GVI vs. NDVI in buffers with different radii had strong positive correlations, with a maximum Spearman coefficient of 0.77 for the 15 m NDVI buffer. Correlation coefficients decreased progressively from urban (0.77) to intermediate (0.72) and rural (0.66) areas. Correlation coefficients strongly decreased with increasing buffer sizes. Analysis of GSV images with high NDVI but low GVI values indicates that greenery types and building distributions significantly affect the street-level visible greenery. This study links street-level greenery with features in the built environment by using different methods for assessing green exposure. The findings provide methodological insights for greenery exposure studies and inform evidence-based urban planning strategies for optimizing green visibility.

Yanrong Chen, Zhiwen Shi, Anwar Eziz et al.

High-accuracy Digital Elevation Models (DEMs) are critical for hydrological and ecological applications in low-relief arid basins, yet Interferometric Synthetic Aperture Radar (InSAR)-derived DEMs suffer from significant altitudinal errors due to temporal decorrelation and phase unwrapping artifacts, particularly in flat terrains. To address these limitations, we developed a novel machine learning framework that synergizes Sentinel-1 InSAR, UAV photogrammetry, Sentinel-2 spectral indices, and ALOS topographic features to enhance DEM accuracy. The approach was validated in Northwest China’s Taitema Lake basin across 13 sample plots covering diverse arid surface types (dunes, wetlands, playas). Four algorithms – Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), and Polynomial Regression (PR) – were rigorously evaluated. Without topographic data, SVM achieved the highest accuracy (test-set R2 = 0.8564). Integrating terrain features with RF further improved performance (R2 = 0.8634, MAE = 1.0683 m), reducing errors from approximately [−10, 27] m to predominantly ±6 m. The RF-corrected DEM exhibited a 42.8% decrease in standard deviation (2.60 m → 1.49 m) and a substantial R2 increase (16.4% → 89.1%). Shapley Additive exPlanations (SHAP) interpretability analysis identified slope and near-infrared reflectance as dominant error-correction features. The corrected DEMs demonstrate enhanced terrain continuity, minimized elevation noise, and offer a scalable, efficient solution for InSAR post-processing in ecologically sensitive arid regions.

Wandong Jiang, Yuli Sun, Lin Lei et al.

Change detection (CD) is essential in remote sensing (RS) for natural resource monitoring, territorial planning, and disaster assessment. With the abundance of data collected by satellite, aircraft, and unmanned aerial vehicles, the utilization of multisource RS image CD (RSICD) enables the efficient acquisition of ground object change information and timely updates to existing databases. Although CD techniques have been developed and successfully applied for approximately six decades, a systematic and comprehensive review that addresses emerging trends, including multisource, data-driven, and large-scale artificial intelligence (AI) models, is lacking. Therefore, first, the development process of RSICD was reviewed. Second, the characteristics of multisource RS images were analyzed, and all publicly available RSICD data that we could gather were collected and organized. Third, RSICD methods were systematically classified and summarized on the basis of the detection framework, detection granularity, and data sources. Fourth, the suitability of specific data and CD methods for diverse applications and tasks was assessed. Finally, challenges, opportunities, and future directions for RSICD were discussed within the context of high-resolution imagery, multisource data, and large-scale AI models. This review can help researchers better understand this field, shed light on this topic, and inspire further RSICD research efforts.

Kaidan Shi, Yanan Su, Jinhao Xu et al.

Reservoirs play a critical role in terrestrial hydrological systems, but the contribution of small and medium-sized ones is rarely considered and recorded. Particularly in developing countries, there is a rapid increase of such reservoirs due to their quick construction. Accurately identifying these reservoirs is important for understanding social and economic development, but distinguishing them from other natural water bodies poses a significant challenge. Thus, we propose a method to identify reservoirs using high-resolution satellite images and deep learning algorithms. We trained models with various parameters and network structures, and You Only Look Once version 7 (YOLOv7) outperformed other algorithms and was selected to build the final model. The method was applied to a region in northwestern Iran, characterized by an abundance of reservoirs of various sizes. Evaluation results indicated that our method was highly accurate (mAP: 0.79, Recall: 0.76, Precision: 0.82). The YOLOv7 model was able to automatically identify 650 reservoirs in the entire study region, indicating that the proposed method can accurately detect reservoirs and has the potential for broader-scale surveys, even global applications.

Yilei Liu, Xin Pan, Jiangping Liu et al.

ABSTRACTAccurate identification and classification of forage grass are pivotal in optimizing forage resources and breeding superior forage varieties. Given the low accuracy in forage image identification and classification, and the loss of some features from preprocessing, we proposed an innovative approach that integrates preprocessing operations directly into the model instead of preceding feature analysis. We captured near-ground hyperspectral imagery of forage in the field and applied two deep learning models – Squeeze and Excitation ResNet (SEResNet) and Convolution Block Attention Module ResNet (CBAMResNet). These models not only harness the automatic learning capabilities of the ResNet deep network but also employ channel attention and a channel-plus-space dual attention mechanism to filter and label important features. This approach enhances data extraction and analysis, strengthen the correlation between the channel and space dimensions while eliminating redundancy and noise. We compared the performance of the proposed methods with the current popular methods by six evaluation parameters, including overall accuracy (OA), average accuracy (AA), Kappa coefficient, etc. Experiment results show the OA of SEResNet and CBAMResNet are 96.57% and 98.35% respectively. The experiments demonstrate the feasibility of incorporating preprocessing into the network and the effectiveness of the new idea for the classification research of forage.

Dali Wang, Xiaochong Tong, Chenguang Dai et al.

The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty. This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts. It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion. The main contributions of this paper include: (1) It proposes a convolved method for ST-Voxel, which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts. Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts; (2) It realizes the unknown event discovery based on voxelized spatiotemporal information association. Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts, which is conducive to discovering spatiotemporal events. The selection of convolution parameters in voxel modeling is also discussed. A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given.

Yuting Zhao, Jungho Im, Zhen Zhen et al.

Accurate quantification of individual tree parameters is vital for precise forest inventory and sustainable forest management. However, in dense forests, terrestrial laser scanning (TLS), which can provide accurate and detailed forest structural measurements, is limited to capturing the complete tree structure due to the lack of upper canopy views, resulting in an underestimation of tree height. Combining TLS with unmanned aerial vehicle laser scanning (ULS) is an effective way to overcome this limitation. Thus, it is vital to register multi-platform Light Detection and Ranging (LiDAR) data for various forestry applications. This study proposed three automated and nearly parameter-free optimized coarse-to-fine algorithms (i.e. FPFH-based optimized ICP (F-OICP), RANSAC-based optimized ICP (R-OICP), and NDT-based optimized ICP (N-OICP)) to accurately register TLS and ULS point data for individual tree crown delineation and parameters (diameter at breast height (DBH) and tree height) estimations in different forest types (i.e. coniferous, mixed broadleaf-coniferous, and broadleaf). Results showed that the proposed optimized algorithms had a good registration performance, with an average RMSE of about 8.3 cm for the transformation error; and obtained stable and high accuracies of individual tree crown delineation (ITCD) (F-score: 0.7), DBH (R2: 0.9, RMSE <1.85 cm), and tree height (R2: 0.8, RMSE <0.37 m) estimates for three forest types. F-OICP performed the best in tree height estimation, reducing the RMSE by 48%, 12%, and 12% compared to iterative closest point (ICP), R-OICP, and N-OICP, respectively. Stand type significantly impacted ITCD and individual tree parameter estimations. The ITCD and DBH estimation accuracy of coniferous forests were marginally higher than those of broadleaf forests (F-score: 0.78 vs. 0.78, DBH RMSE: 1.57 vs. 1.74), while those of mixed broadleaf-coniferous forests were the lowest (F-score: 0.71, DBH RMSE: 2.19). The accuracies of tree height estimates in coniferous forests were the highest (R2: 0.87, RMSE: 0.21 m), followed by mixed broadleaf-coniferous (R2: 0.84, RMSE: 0.37 m) and broadleaf (R2: 0.84, RMSE: 0.44 m) forests. This work developed automated, nearly parameter-free, and effective registration algorithms and recommended F-OICP to be the most appropriate for dense forests (i.e. natural secondary forests). The optimized registration algorithms facilitate the ability for the synergistic use of multi-platform LiDAR and offer appealing and promising approaches for future accurate quantification of individual tree parameters, efficient forest inventories, and sustainable forest management.

IBRAHIM MWATHANE, Mwenda Makathimo, Robert Kibugi et al.

This paper was presented in the 2021 Conference on Land Policy in Africa held in Kigali, Rwanda, in November 2021. It is based on a three-year study by the Land Development and Governance Institute (LDGI), in partnership with the International Development Research Centre (IDRC), Canada, to test the efficacy of the application of Kenya's new Community Land Act. The study sites are in Isiolo and Marsabit Counties, both in the Arid and Semi-Arid (ASAL) Northern Kenya. The study results demonstrate the importance of adequate sensitisation of the key actors (government, political and community) at county level and the grass root communities, the use of participatory and inclusive processes to establish the community governance organs and fulfil the statutory requirements provided under this new law. The study also highlights the importance of the use of community champions to ensure the continuous sensitisation of community members, and to to galvanise the communities in the registration and management of their land. Through the study, communities were supported to develop basic tools to guide them in land use planning and investor negotiations. The land use planning guide developed will help the communities to liase with the county government to prepare a land use development plan which is expected to enhance the sustainable use of the community land, while the investor negotiation guide developed will be helpful during negotiations with investors interested in partnering with the communities for investments on their land. The use of the investor guides is expected to inform the preparation of mutually beneficial investor agreements as anticipated under the Community Land Act. It is expected that the lessons from the study, which include: community empowerment, use of participatory inclusive processes, ensuring gender equity in the composition of governance organs and in decision making processes, embracing the youth, use of champions and avoiding the negative impacts of the adjudication of community land will be useful to state and non-state implementers of the new law, and may be used to inform the scaling up implementation countrywide. It is also expected that gaps identified in the new law, such as the management of the inheritance rights of children married outside the community, and those divorced, will inform law review.

Matheus Henrique Lisboa, Afonso de Paula dos Santos, Nilcilene das Graças Medeiros et al.

No Brasil, a avaliação da acurácia posicional de produtos cartográficos segue as diretrizes do Decreto nº. 89.817/1984. Esse decreto divide a acurácia posicional em duas componentes: planimétrica e altimétrica. Produtos tridimensionais, como Modelos Digitais de Superfície/Elevação (MDS/MDE), acabam sendo avaliados em componentes separadamente, mas alguns autores, tais como Santos (2015) e Li et al. (2005) demonstraram que a forma mais eficiente de se avaliar esse tipo de produto é por meio da resultante entre as componentes planimétricas e altimétricas. Sendo assim, este trabalho propõe um método para a avaliação da acurácia tridimensional de produtos cartográficos, por meio das componentes tridimensionais de uma superfície geométrica, no caso em estudo, um elipsoide, cujas dimensões são dadas pelas tolerâncias descritas no Decreto nº. 89.817/1984. Posteriormente, o método proposto (chamado de EPSI) foi confrontado com a metodologia do Decreto nº. 89.817/1984, em conjunto com a ET-CQDG (DSG, 2016). Para verificar a eficiência do método, foram simuladas 15.000 discrepâncias e, em aproximadamente 83% dos casos, o método proposto foi mais restritivo se comparado à avaliação da planimetria, e, em 58% dos casos, quando comparado à análise da altimetria. No restante dos casos, o método se apresentou equivalente à análise separada da planimetria e altimetria, descrita pela ET-CQDG (DSG, 2016). Utilizando exemplos práticos, percebe-se que a metodologia tridimensional é mais restritiva que a usualmente aplicada.

Chen-Ling J. Hung, L. Allan James, Michael E. Hodgson

Buildings, as impervious surfaces, are an important component of total impervious surface areas that drive urban stormwater response to intense rainfall events. Most stormwater models that use percent impervious area (PIA) are spatially lumped models and do not require precise locations of building roofs, as in other applications of building maps, but do require accurate estimates of total impervious areas within the geographic units of observation (e.g. city blocks or sub-watershed units). Two-dimensional mapping of buildings from aerial imagery requires laborious efforts from image analysts or elaborate image analysis techniques using high spatial resolution imagery. Moreover, large uncertainties exist where tall, dense vegetation obscures the structures. Analyzing LiDAR point-cloud data, however, can distinguish buildings from vegetation canopy and facilitate the mapping of buildings. This paper presents a new building extraction approach that is based on and optimized for estimating building impervious areas (BIA) for hydrologic purposes and can be used with standard GIS software to identify building roofs under tall, thick canopy. Accuracy assessment methods are presented that can optimize model performance for modeling BIA within the geographic units of observation for hydrologic applications. The Building Extraction from LiDAR Last Returns (BELLR) model, a 2.5D rule-based GIS model, uses a non-spatial, local vertical difference filter (VDF) on LiDAR point-cloud data to automatically identify and map building footprints. The model includes an absolute difference in elevation (AdE) parameter in the VDF that compares the difference between mean and modal elevations of last-returns in each cell. The BELLR model is calibrated for an extensive inner-city, highly urbanized small watershed in Columbia, South Carolina, USA that is covered by tall, thick vegetation canopy that obscures many buildings. The calibration of BELLR used a set of building locations compiled by photo-analysts, and validation used independent building reference data. The model is applied to two residential neighborhoods, one of which is a residential area within the primary watershed and the other is a younger suburban neighborhood with a less-well developed tree canopy used as a validation site. Performance results indicate that the BELLR model is highly sensitive to concavity in the lasboundary tool of LAStools® and those settings are highly site specific. The model is also sensitive to cell size and the AdE threshold values. However, properly calibrated the BIA for the two residential sites could be estimated within 1% error for optimized experiments. To examine results in a hydrologic application, the BELLR estimated BIAs were tested using two different types of hydrologic models to compare BELLR results with results using the National Land Cover Database (NLCD) 2011 Percent Developed Imperviousness data. The BELLR BIA values provide more accurate results than the use of the 2011 NLCD PIA data in both models. The VDF developed in this study to map buildings could be applied to LiDAR point-cloud filtering algorithms for feature extraction in machine learning or mapping other planar surfaces in more broad-based land-cover classifications.

Gladys Villegas, Wenzhi Liao, Ronald Criollo et al.

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.

Jesã Pereira Kreitlow, João dos Santos Vila da Silva, Sandra Mara Alves da Silva Neves et al.

O escopo desta pesquisa foi avaliar a vulnerabilidade ambiental e os confl itos decorrentes da utilização da terra da municipalidade de Mirassol D'Oeste, na perspectiva da geração de subsídios para o planejamento municipal. O estudo foi desenvolvido através da utilização de metodologias que avaliam o potencial à erosão hídrica laminar, a capacidade de uso da terra e o confl ito de uso da terra para a obtenção da vulnerabilidade ambiental. Foram individualizados 12 morfocompartimentos, sendo que destes os de número 5, 8 e 10 são os que apresentam maior susceptibilidade à erosão devido a declividades superiores a 45% e a presença de Neossolos Quartzarênicos. Em geral o município apresenta médio potencial à erosão laminar, sendo que esta classe ocupa aproximadamente 70% da extensão territorial municipal. Na área de estudo a capacidade de uso com maior representatividade foi a IV, esta classe é composta por terras que são cultiváveis ocasionalmente, onde são encontrados problemas complexos de conservação. A classe de confl ito baixo é a de maior ocorrência (84,95%) na área pesquisada. De posse deste estudo os planejadores locais podem decidir sobre as melhores formas de utilização das áreas rurais do município.

Anderson Renato Viski, Claudia Pereira Krueger, Tobias Bleninger

O efeito multicaminho é um problema no posicionamento geodésico estático ou cinemático, ele pode causar erros na determinação de coordenadas geodésicas que podem variar de poucos milímetros até metros. Entre as várias técnicas existentes para atenuar este efeito foi desenvolvido, para este fim, um protótipo de um material denominado AEM (Atenuador do Efeito de Multicaminho). Ele visa absorver ondas atenuadas. Nesta pesquisa verificou-se a influência do efeito multicaminho no posicionamento estático e cinemático sobre massas de água doce empregando-se dois métodos para análise da redução deste efeito. Utilizaram-se materiais isolantes eletromagnéticos e antena do tipo Choke Ring, os quais foram acoplados sobre plataformas do tipo Eulerianas.

Ana Clara Mourão Moura, Gerson José de Mattos Freire

O estudo visa à apresentação de reflexões sobre o contexto do surgimento das normativas de ordenamento territorial no Brasil, que criam as bases para a promulgação da Portaria Ministerial que instituiu o Cadastro Territorial Multifinalitário. Contextualiza a importância e os motivadores da instalação do princípio no Brasil, com ênfase na promulgação da Constituição Federal e do Estatuto da Cidade. Defende o papel do Cadastro Territorial Multifinalitário como ferramenta fundamental para que os instrumentos e princípios do Estatuto da Cidade sejam materializados na forma de ordenamento territorial. Explica os conceitos principais da Portaria Ministerial que dá as diretrizes para o Cadastro Territorial Multifinalitário. Elege o instrumento de EIV - Estudo de Impacto de Vizinhança - para demonstrar que o uso do Cadastro Territorial Multifinalitário é condição sine qua non para os novos estudos e projetos de planejamento e gestão urbana.

Agata Lo Tauro

A seguito di uno studio iniziato nel 1993 presso la Manchester University, su city centres italiani ed inglesi, è stata avviata una indagine sulle trasformazioni diacroniche e sincroniche delle principali tipologie edilizie utilizzando analisi archivistiche, storiche e strumenti normativi, in primis coniugate con la computer graphics e nella mid-term phase con la geomatica in genere. La fase finale della ricerca ha evidenziato la necessità di implementare strategie di “manutenzione programmata” capaci di “prevenire piuttosto che curare”, utilizzando varie tipologie di Open Data implementando approcci pluridisciplinari. Come case-study è stato scelto il centro storico di Acireale. Typological and morphological analysis to support the mainte-nance program with the help of cadastral surveys Following a study that began in 1993 at Manchester Univer-sity, city centers in Italy and UK started an investigation on the diachronic transformations and synchronic analysis of the main building using archival, historical and regulatory instruments, primarily conjugated with computer graphics and in the mid-term phase with the geomatics in general. The final phase of the  research  highlighted  the  need  to  implement  strategies for "scheduled maintenance" capable of  "prevention rather than cure", using various types of Open Date implementing multidisciplinary approaches. As a case study was chosen the historical center of Acireale.

Ona Vileikis, Mario Santana Quintero, Koen Van Balen et al.

This paper discusses the application of Information Management Systems (IMS) in cultural heritage. IMS offer a set of tools for understanding, inventorying and documenting national, regional and World Heritage properties.  Information Management Systems can assist State Parties, stakeholders and heritage site managers involved in cultural heritage management and conservation by easily mining, sharing and exchanging information from multiple sources based on international standards. Moreover, they aim to record, manage, visualize, analyze and disseminate heritage information. In close collaboration with five Central Asian countries, namely, Turkmenistan, Kazakhstan, Kyrgyzstan, Uzbekistan and Tajikistan; a Belgian consortium headed by the Raymond Lemaire International Centre for Conservation (RLICC), K.U.Leuven is developing the Silk Roads Cultural Heritage Resource Information System (CHRIS). This Web-based Information Management System supports the preparation of the Central Asia Silk Roads serial and transnational nominations on the UNESCO World Heritage list. The project has been set up thanks to the financial support of the Belgian Federal Science Policy Office (BELSPO) and in collaboration with UNESCO World Heritage Centre in conjunction with the People’s Republic of China and the Japanese Funds-in-Trust UNESCO project. It provides a holistic approach for the recording, documenta tion, protection and monitoring tasks as part of the management of these potential World Heritage Properties. The Silk Roads CHRIS is easily accessible to the general user, presented in a bilingual English and Russian frame and interoperable, i.e. open for other applications to connect to. In this way, all information for the nomination dossiers is easily verified regarding consistency and quality and ready for managing, periodic reporting and monitoring processes in the respect to the property listed. Fina lly, this study provides a general framework to establish the effectiveness and limits of the use of information systems for serial transnational nominations of World Heritage Properties and to demonstrate the potentials of an improved heritage documentation system.

H. Bernhard

No abstract available.

A. Kilchenmann

No abstract available.

Redazione Redazione

La conferenza della comunità GIS italiana senza ombra di dubbio può essere assimilata alla annuale conferenza degli Utenti ESRI, che per presenza e temi non ha eguali ne come appuntamento nè come presenze. Non ce ne vogliano gli altri players delle tecnologie GIS, nè tanto meno gli organizzatori di altre conferenze nazionali quale ASITA

H. H. Boesch

No abstract available.