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Journal articles on the topic 'Google earth image'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

Carolina, Sparavigna Amelia. "The Shrinking Toshka Lakes in the Google Earth Images." International Journal of Sciences Volume 2, no. 2013-08 (2013): 92–94. https://doi.org/10.5281/zenodo.3348418.

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Toshka Lakes are lakes artificially created in the Sahara Desert of Egypt, by the water of the Nile, conveyed from the Nasser Lake through a canal in the Toshka Depression. From space, astronauts noticed the growing of a first lake, the easternmost one, in 1998. Then additional lakes grew in succession due west, the westernmost one between 2000 and 2001. The pictures of the Toshka Lakes taken by the crews of space missions and the satellite imagery can show the evolution of them. From 2006, the lakes started shrinking rapidly. The recent images, among them those of Google Earth, display that t

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Najaf, M., H. Arefi, H. Amini Amirkolaee, and B. Farajelahi. "MONOCULAR DEPTH ESTIMATION OF GOOGLE EARTH IMAGES USING CONVOLUTIONAL NEURAL NETWORKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W1-2022 (January 14, 2023): 589–94. http://dx.doi.org/10.5194/isprs-annals-x-4-w1-2022-589-2023.

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Abstract. Depth estimation from images is an important task using scene understanding and reconstruction. Recently, encoder-decoder type fully convolutional architectures have gained great success in the area of depth estimation. Depth extraction from aerial and satellite images is one of the important topics in photogrammetry and remote sensing. This is usually done using image pairs, or more than two images. Solving this problem using a single image is still a challenging problem and has not been completely solved. Several convolutional neural networks have been proposed to extract depth fro

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Wang, Wenliang. "Automatic extraction of coastline based on Google Earth engine." International Journal of Computer Science and Information Technology 1, no. 1 (2023): 102–11. http://dx.doi.org/10.62051/ijcsit.v1n1.14.

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Traditional remote sensing image-based coastline extraction is limited by data volume and processing speed, and the extracted coastline is susceptible to noise. Therefore, this paper proposes a method based on the Google Earth Engine geospatial platform, which combines threshold segmentation, the Otsu method, and morphological algorithms. First, remote sensing images are preprocessed on the platform, and the Normalized Difference Water Index (NDWI) is calculated. Then, the Otsu method is used to calculate the NDWI threshold for water-land segmentation, resulting in a binary water-land image. N

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Mr., Sadashiv D. Lavange* 1. Prof. A. D. Vishwakarma 2. Prof. H. T. Ingale 3. &. Prof. V. D. Chaudhari 4. "IMAGE LOCALIZATION USING GOOGLE MAPS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 423–26. https://doi.org/10.5281/zenodo.828673.

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Now days, government of India is making awareness about green energy. So, various analyses have to make for promotion of green energy like solar energy over the country. It is required to know about the rooftop area useful for holding solar panels. Image localization and image registration techniques are the mostly used by the researchers for searching image in specified place. Google earth image searching is required for localization and analysis of specific area over the areal image. This type of searching is useful for calculating the area of solar rooftop in any specific location. Within t

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Mr., Sadashiv D. Lavange* 1. Prof. A. D. Vishwakarma 2. Prof. H. T. Ingale 3. &. Prof. V. D. Chaudhari 4. "IMAGE LOCALIZATION USING GOOGLE MAPS." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 7 (2017): 585–88. https://doi.org/10.5281/zenodo.829780.

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Now days, government of India is making awareness about green energy. So, various analyses have to make for promotion of green energy like solar energy over the country. It is required to know about the rooftop area useful for holding solar panels. Image localization and image registration techniques are the mostly used by the researchers for searching image in specified place. Google earth image searching is required for localization and analysis of specific area over the areal image. This type of searching is useful for calculating the area of solar rooftop in any specific location. Within t

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Guerriero, Luigi, Diego Di Martire, Domenico Calcaterra, and Mirko Francioni. "Digital Image Correlation of Google Earth Images for Earth’s Surface Displacement Estimation." Remote Sensing 12, no. 21 (2020): 3518. http://dx.doi.org/10.3390/rs12213518.

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An increasing number of satellite platforms provide daily images of the Earth’s surface that can be used in quantitative monitoring applications. However, their cost and the need for specific processing software make such products not often suitable for rapid mapping and deformation tracking. Google Earth images have been used in a number of mapping applications and, due to their free and rapid accessibility, they have contributed to partially overcome this issue. However, their potential in Earth’s surface displacement tracking has not yet been explored. In this paper, that aspect is analyzed

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Schmitt, M., L. H. Hughes, C. Qiu, and X. X. Zhu. "AGGREGATING CLOUD-FREE SENTINEL-2 IMAGES WITH GOOGLE EARTH ENGINE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W7 (September 16, 2019): 145–52. http://dx.doi.org/10.5194/isprs-annals-iv-2-w7-145-2019.

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<p><strong>Abstract.</strong> Cloud coverage is one of the biggest concerns in spaceborne optical remote sensing, because it hampers a continuous monitoring of the Earth’s surface. Based on Google Earth Engine, a web- and cloud-based platform for the analysis and visualization of large-scale geospatial data, we present a fully automatic workflow to aggregate cloud-free Sentinel-2 images for user-defined areas of interest and time periods, which can be significantly shorter than the one-year time frames that are commonly used in other multi-temporal image aggregation approache

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Prayogo, Luhur Moekti. "Mangrove Vegetation Mapping Using Sentinel-2A Imagery Based on Google Earth Engine Cloud Computing Platform." International Journal of Science, Engineering and Information Technology 6, no. 1 (2021): 249–55. http://dx.doi.org/10.21107/ijseit.v6i1.12175.

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Mangroves are trees whose habitat is affected by tides, and their presence has decreased from year to year. Today, mapping technology has undergone many developments, including the availability of images of various resolutions and cloud-based image processing. One of the popular platforms today is the Google Earth Engine. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for extensive processing. The advantage of using Google Earth Engine is that users do not have to be IT experts without experts in application development, WEB prog

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Qian, Junhao, Min Xia, Yonghong Zhang, Jia Liu, and Yiqing Xu. "TCDNet: Trilateral Change Detection Network for Google Earth Image." Remote Sensing 12, no. 17 (2020): 2669. http://dx.doi.org/10.3390/rs12172669.

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Change detection is a very important technique for remote sensing data analysis. Its mainstream solutions are either supervised or unsupervised. In supervised methods, most of the existing change detection methods using deep learning are related to semantic segmentation. However, these methods only use deep learning models to process the global information of an image but do not carry out specific trainings on changed and unchanged areas. As a result, many details of local changes could not be detected. In this work, a trilateral change detection network is proposed. The proposed network has t

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Chen, Yao, and Xiaoning Chen. "Image interpolation of gas concentration based on Google Earth." Journal of Physics: Conference Series 1176 (March 2019): 022018. http://dx.doi.org/10.1088/1742-6596/1176/2/022018.

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Wang, Shu Ting. "Research on the Using Electronic Map Software Google Earth in the Geographical Class." Applied Mechanics and Materials 543-547 (March 2014): 2996–99. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2996.

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Based on a brief introduction of electronic map software Google Earths functions related to teaching, teaching characteristics of Google Earth (GE), "GE is more interactive, dynamic than traditional map", were discussed. And then focusing on methods how to use GE in the geographical class, search maps, production and management landmarks, map overlay, image overlay, and making geography picture courseware are presented. Finally, Application Strategies about GE in geography teaching are mentioned.

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Sartori, J. A., J. B. Sbruzzi, and E. L. Fonseca. "PRELIMINARY ANALYSIS FOR AUTOMATIC TIDAL INLETS MAPPING USING GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (November 6, 2020): 249–53. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-249-2020.

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Abstract. This work aims to define the basic parameters for the automatic mapping of the channel between the Lagoa do Peixe and the Atlantic Ocean, which is located in the municipalities of Tavares and Mostardas, Rio Grande do Sul state, Brazil. The automatic mapping is based on an unsupervised classification of Landsat 8 satellite images at the Google Earth Engine cloud computing platform. The images used were selected to present both channel situations (opened and closed). Three images were selected with acquisition dates that presented the open channel and three that presented the closed ch

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Juan Vincent Elfonda, Vikhory Bagus Wahyu Nugroho, and Tuhu Agung Rachmanto. "Klasifikasi Tutupan Lahan di Kabupaten Trenggalek Menggunakan Google Earth Engine." Jurnal Kendali Teknik dan Sains 2, no. 3 (2024): 85–94. http://dx.doi.org/10.59581/jkts-widyakarya.v2i3.3488.

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Land cover is defined as the physical and biological cover of the earth's surface, both those formed naturally such as swamps, hills and rivers and those formed by man-made means such as rice fields, gardens, forests and buildings. As technology develops, conventional methods of satellite image processing are starting to be abandoned. This is because conventional methods require quite a long time to process satellite image data. The presence of Google Earth Engine (GEE), which is a cloud computing-based platform, makes it easier for users to process satellite image data boldly and for free. Th

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Munandar, Erik, Agitha Saverti Jasmine, and Muta Ali Khalifa. "Identification of Mangrove Cover in Banten Bay using Google Earth Engine." Journal of Applied Geospatial Information 7, no. 1 (2023): 796–99. http://dx.doi.org/10.30871/jagi.v7i1.5326.

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The existence of mangroves is a factor in the natural preservation of an area. The goal of this research is to identify the mangrove forest cover in Teluk Banten using guided classification based on machine learning available in GEE.The method used in this research is to visually analyze the spectral value of Sentinel 2A. The composite images used in the analysis include Bands 8A114 and Bands 8A115. Determination of subset images (cropping) is carried out to accommodate the size of the image according to the size of the research location to determine its distribution, extent and changesMangrov

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Ballagh, Lisa M., Mark A. Parsons, and Ross Swick. "Visualising cryospheric images in a virtual environment: present challenges and future implications." Polar Record 43, no. 4 (2007): 305–10. http://dx.doi.org/10.1017/s0032247407006523.

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ABSTRACTThe United States National Snow and Ice Data Center (NSIDC) initiated an outreach project to enhance the visibility of and interest in cryospheric images. Methods were utilised to convert cryospheric data into a projection and image format compatible with Google Earth™. The word ‘image’ should be emphasised since raster data in a native polar projection and format cannot be overlaid on the Earth without prior data conversions. The project focused on reaching out to a diverse audience by integrating images from key components of the cryosphere into a single compressed Keyhole Markup Lan

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Guo, J., H. J. Tu, H. Li, Y. Zhao, and J. Zhou. "HORIZONTAL ACCURACY ASSESSMENT OF GOOGLE EARTH DATA OVER TYPICAL REGIONS OF AUSTRALIA USING WORLDVIEW." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2021 (June 29, 2021): 763–68. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2021-763-2021.

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Abstract. Since the release of Google Earth image data, it has been the most widely used remote sensing data worldwide, and its accuracy evaluation has also been the focus of historical research. However, the researchers found that Google Earth's image accuracy assessment results have obvious regional characteristics. This article uses the Australian continent as the research area and WorldView-2 remote sensing images as reference data to study the accuracy evaluation results of Google Earth data. The research shows that the overall accuracy of the assessment area in Australia is better. The a

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Sparavigna, Amelia Carolina. "Sinkholes of Dead Sea in Satellite Image Time Series." PHILICA 2016 (February 20, 2016): 575. https://doi.org/10.5281/zenodo.3368843.

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Carolina, Sparavigna Amelia. "Analysis of the Motion of Some Brazilian Coastal Dunes." International Journal of Sciences Volume 5, no. 2016-01 (2016): 22–31. https://doi.org/10.5281/zenodo.3349089.

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Satellite images of coastal dunes of Brazil are showing an interesting feature. As we can see from images of Google Earth, these dunes are leaving, during their motion, some footprints behind, which are sedimentary patterns. Then, we can use the Google Earth images to follow year after year the motion of such dunes. Here we give some examples of the analysis we can perform on such satellite images, in particular for a remote monitoring of these moving dunes.Read Complete Article at ijSciences: V5201601905 AND DOI: http://dx.doi.org/10.18483/ijSci.905

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Felipe, Alexandre Luis Da Silva, and Lincoln Gehring Cardoso. "AVALIAÇÃO DE ÁREA E DISTÂNCIAS EM PLANTA OBTIDA VIA GOOGLE EARTH." ENERGIA NA AGRICULTURA 32, no. 2 (2017): 189. http://dx.doi.org/10.17224/energagric.2017v32n2p189-194.

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O presente trabalho objetivou avaliar distâncias horizontais e área de um polígono obtido através de imagem do Google Earth tendo como referência levantamento topográfico realizado em campo utilizando-se Estação Total. O processamento dos dados do levantamento topográfico foi realizado através do programa computacional DataGeosis versão Office que acusou elevada precisão, garantindo sua condição de referência. As coordenadas obtidas pelo Google Earth foram submetidos ao software AutoCAD 13 para desenho. Com os dados assim obtidos foi possível a geração de plantas bem como cálculo de distâncias

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Nadagouda, Kalyani. "Digital image processing for urban sprawl classification in google earth." i-manager’s Journal on Image Processing 9, no. 1 (2022): 23. http://dx.doi.org/10.26634/jip.9.1.18573.

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Maps of land use and land cover are necessary for studying how the earth's surface changes over time and how human activities affect their surroundings the expanding pool of available resources of remote sensing, particularly the wellarchived Sentinel-2B images with resolution of 10 metres were used to conduct the research. For land use and land cover maps, supervised categorization is used. However, ground truth is required to attain high classification accuracy. There is a need for high-quality samples in big quantities. It takes time and effort to collect ground truth samples. When it comes

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Mustaqim, Riza Afrian. "Penggunaan Google Earth Sebagai Calibrator Arah Kiblat." Jurnal Justisia : Jurnal Ilmu Hukum, Perundang-undangan dan Pranata Sosial 6, no. 2 (2021): 194. http://dx.doi.org/10.22373/justisia.v6i2.11537.

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Qibla direction calibration is something that is very urgent in order to achieve accuracy and accuracy in facing the Qibla. Technological developments in the industrial era 4.0 require the use of technology that is easier to carry out the calibration process. Google earth which displays a virtual image of the actual earth can be an alternative in fulfilling this. This study provides a detailed description of the use of Google Earth as a Qibla direction calibrator. The method used in this research is descriptive analytical with a scientific approach. The results of this study indicate that goog

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Ruzgienė, Birutė, Qian Yi Xiang та Silvija Gečytė. "LARGE SCALE CITY MAPPING USING SATELLITE IMAGERY / KOSMINIŲ NUOTRAUKŲ IŠ GOOGLE EARTH, TAIKOMŲ MIESTAMS KARTOGRAFUOTI STAMBIUOJU MASTELIU, REKTIFIKAVIMAS / РЕКТИФИКАЦИЯ КОСМИЧЕСКИХ СНИМКОВ ИЗ GOOGLE EARTH ДЛЯ КРУПНОМАСШТАБНОГО КАРТОГРАФИРОВАНИЯ ГОРОДОВ". Geodesy and Cartography 37, № 4 (2012): 168–71. http://dx.doi.org/10.3846/13921541.2011.645348.

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The rectification of high resolution digital aerial images or satellite imagery employed for large scale city mapping is modern technology that needs well distributed and accurately defined control points. Digital satellite imagery, obtained using widely known software Google Earth, can be applied for accurate city map construction. The method of five control points is suggested for imagery rectification introducing the algorithm offered by Prof. Ruan Wei (tong ji University, Shanghai). Image rectification software created on the basis of the above suggested algorithm can correct image deforma

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Lee, M. J., and S. Y. Park. "3D SURFACE RECONSTRUCTION FROM MULTI-VIEW AND MULTI-DATE GOOGLE EARTH SATELLITE IMAGES WITH 3D HOMOGRAPHY-BASED PROJECTIVE RECONSTRUCTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022 (May 30, 2022): 135–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2022-135-2022.

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Abstract. In this paper, we propose the 3D surface reconstruction scheme using multi-view and multi-date Google Earth (GE) satellite images. Multi-view stereo matching (MVS) scheme is one of the methods for reconstructing dense 3D surface based on multi-view images and corresponding camera pose geometry. If many views are input, MVS can estimate the disparity (depth) by matching pixels. However, the common users are not always possible to obtain both multi-view satellite images and the camera geometry (such as Rational Polynomial Camera) in various earth regions. Instead, the GE provides multi

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Xu, R. G., G. Qiao, Y. J. Wu, and Y. J. Cao. "EXTRACTION OF RIVERS AND LAKES ON TIBETAN PLATEAU BASED ON GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 1797–801. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-1797-2019.

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<p><strong>Abstract.</strong> Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the

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Yun, Min-Sub, Woo-Jeoung Nam, and Seong-Whan Lee. "Coarse-to-Fine Deep Metric Learning for Remote Sensing Image Retrieval." Remote Sensing 12, no. 2 (2020): 219. http://dx.doi.org/10.3390/rs12020219.

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Remote sensing image retrieval (RSIR) is the process of searching for identical areas by investigating the similarities between a query image and the database images. RSIR is a challenging task owing to the time difference, viewpoint, and coverage area depending on the shooting circumstance, resulting in variations in the image contents. In this paper, we propose a novel method based on a coarse-to-fine strategy, which makes a deep network more robust to the variations in remote sensing images. Moreover, we propose a new triangular loss function to consider the whole relation within the tuple.

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Ashish, Aggarwal, Rishabh, and Singh Ekta. "Land use/land cover change detection using remote sensing and gis based techniques for solani river basin, uttarakhand." i-manager’s Journal on Civil Engineering 12, no. 4 (2022): 42. http://dx.doi.org/10.26634/jce.12.4.19070.

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Land Use/Land Cover (LU/LC) change detection was performed in the Solani river watershed area using multi-temporal remote sensing images (Landsat 8 ETM+ image of year 2014 and Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) image of year 2021). Image classification and change detection were carried out for both images using Arc Geographic Information System (GIS) 10.1 and Earth Resources Data Analysis System (ERDAS) Imagine 2016 software. High-resolution Google Earth imagery and Land Remote-Sensing Satellite (LANDSAT) images were used for the accuracy assessment of the cla

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De Brito, Mariana Madruga. "Identificação de Cicatrizes de Movimentos de Massa na Borda Oriental da Bacia do Paraná com base em Imagens Landsat 5-TM (Identification of Landslides Scars in the Eastern Edge of the Paraná Basin)." Revista Brasileira de Geografia Física 8, no. 1 (2015): 056. http://dx.doi.org/10.26848/rbgf.v8.1.p056-070.

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Movimentos de massa são fenômenos naturais caracterizados pelo deslocamento de solo e rocha vertente abaixo. Quando esses processos ocorrem em áreas urbanizadas, podem causar perdas econômicas, impactos sociais e, em casos extremos, perda de vidas humanas. Na tentativa de mitigar tais desastres, torna-se necessário mapear os locais já afetados pelos mesmos, uma vez que escorregamentos recentes podem sugerir futuros padrões de instabilidade. Nesse sentido, este trabalho teve por objetivo mapear as cicatrizes de movimentos de massa em um segmento da Borda Oriental da Bacia do Paraná, por meio do

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Karimov, B., G. Karimova, and N. Amankulova. "Land Cover Classification Improvements by Remote Sensing Data Fusion." Bulletin of Science and Practice, no. 2 (February 15, 2023): 66–74. http://dx.doi.org/10.33619/2414-2948/87/07.

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Computer processing and analysis of satellite data is an urgent task of the science of remote sensing of the earth. Such processing can range from adjusting the contrast and brightness of the images of an amateur photographer to a group of scientists using neural network classification to determine the types of minerals in a hyperspectral satellite image. This article implements a method of satellite data fusion, which improves the digital image interpretation and image quality for further analysis. For fusion, a multispectral image with a resolution of 30 m Landsat 5 with 6 channels was taken

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Luo, Chong, Beisong Qi, Huanjun Liu, et al. "Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine." Remote Sensing 13, no. 4 (2021): 561. http://dx.doi.org/10.3390/rs13040561.

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The purpose of this study was to evaluate the feasibility and applicability of object-oriented crop classification using Sentinel-1 images in the Google Earth Engine (GEE). In this study, two study areas (Keshan farm and Tongnan town) with different average plot sizes in Heilongjiang Province, China, were selected. The research time was two consecutive years (2018 and 2019), which were used to verify the robustness of the method. Sentinel-1 images of the crop growth period (May to September) in each study area were composited with three time intervals (10 d, 15 d and 30 d). Then, the composite

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Carolina, Sparavigna Amelia. "A Case Study of Moving Sand Dunes: The Barchans of the Kharga Oasis." International Journal of Sciences Volume 2, no. 2013-08 (2013): 95–97. https://doi.org/10.5281/zenodo.3348420.

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Recently we have proposed the use of the time series of Google Earth to investigate the motion of sand dunes. In this paper we consider a case study, applying the method to some barchans of the Kharga Oasis. To evaluate the motion of these dunes as a function of their size we use Gimp, a GNU image processing software.Read Complete Article at ijSciences: V2201307241 AND DOI: http://dx.doi.org/10.18483/ijSci.241

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Yogita, Gogawale, Deshpande Ankita, Gupta Pooja, and Gokhale Aditi. "Google Earth Engine based Forest Fire Detection System." International Journal of Innovative Science and Research Technology 7, no. 5 (2022): 771–74. https://doi.org/10.5281/zenodo.6626245.

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Forest fires represent a constant threat to ecological systems and human lives. Past has witnessed various instances of forest and wild land fires. Especially in Amazon Forest. Fires play a remarkable role in determining landscape structure, pattern and eventually the species composition of ecosystems. Fires are considered as a significant environmental issue because they cause prominent economic and ecological damage despite endangering the human lives. So here we are going to develop the system which detects the fire on the earth by using Google earth engine and image processing algorithm wi

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Lei, Rongzhi, Weifang Yang, and Shuwen Yang. "System Development of Making an Image Map Based on Google Earth." IOP Conference Series: Earth and Environmental Science 697, no. 1 (2021): 012004. http://dx.doi.org/10.1088/1755-1315/697/1/012004.

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Yang, Kaixiang, Youming Luo, Mengyao Li, Shouyi Zhong, Qiang Liu, and Xiuhong Li. "Reconstruction of Sentinel-2 Image Time Series Using Google Earth Engine." Remote Sensing 14, no. 17 (2022): 4395. http://dx.doi.org/10.3390/rs14174395.

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Sentinel-2 NDVI and surface reflectance time series have been widely used in various geoscience research, but the data is deteriorated or missing due to the cloud contamination, so it is necessary to reconstruct the Sentinel-2 NDVI and surface reflectance time series. At present, there are few studies on reconstructing the Sentinel-2 NDVI or surface reflectance time series, and these existing reconstruction methods have some shortcomings. We proposed a new method to reconstruct the Sentinel-2 NDVI and surface reflectance time series using the penalized least-square regression based on discrete

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TEODORESCU, Paul, and Simona-Nicoleta VOICU. "Image Classification Using Machine Learning Algorithms in Google Earth Engine Environment." Informatica Economica 25, no. 3/2021 (2021): 5–16. http://dx.doi.org/10.24818/issn14531305/25.3.2021.01.

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Guo, Xianfei, Hui Li, Linhai Jing, and Ping Wang. "Individual Tree Species Classification Based on Convolutional Neural Networks and Multitemporal High-Resolution Remote Sensing Images." Sensors 22, no. 9 (2022): 3157. http://dx.doi.org/10.3390/s22093157.

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The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolu

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Li, Zhixin, Shuang Li, John Anderson, and Jie Shan. "Urban Visual Localization of Block-Wise Monocular Images with Google Street Views." Remote Sensing 16, no. 5 (2024): 801. http://dx.doi.org/10.3390/rs16050801.

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Urban visual localization is the process of determining the pose (position and attitude) of the imaging sensor (or platform) with the help of existing geo-referenced data. This task is critical and challenging for many applications, such as autonomous navigation, virtual and augmented reality, and robotics, due to the dynamic and complex nature of urban environments that may obstruct Global Navigation Satellite Systems (GNSS) signals. This paper proposes a block-wise matching strategy for urban visual localization by using geo-referenced Google Street View (GSV) panoramas as the database. To d

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Lindsay, Erin, Regula Frauenfelder, Denise Rüther, et al. "Multi-Temporal Satellite Image Composites in Google Earth Engine for Improved Landslide Visibility: A Case Study of a Glacial Landscape." Remote Sensing 14, no. 10 (2022): 2301. http://dx.doi.org/10.3390/rs14102301.

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Regional early warning systems for landslides rely on historic data to forecast future events and to verify and improve alarms. However, databases of landslide events are often spatially biased towards roads or other infrastructure, with few reported in remote areas. In this study, we demonstrate how Google Earth Engine can be used to create multi-temporal change detection image composites with freely available Sentinel-1 and -2 satellite images, in order to improve landslide visibility and facilitate landslide detection. First, multispectral Sentinel-2 images were used to map landslides trigg

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Marquez, Renata Moreira, and Wellington Cançado Coelho. "Myopia Index." Surveillance & Society 7, no. 2 (2009): 126–43. http://dx.doi.org/10.24908/ss.v7i2.4139.

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Google Earth is a vision device that operates in the tension between two sets of eyes: vertical and horizontal. The vertical eye locates the observer outside the lived area in a privileged point, far from everyday life. With Google Earth, we have the globe to manipulate with our hands, in a radical disparity of the subject and world. But this world is a set of juxtaposed fragments of images, captured from above. The interval between the limit of resolution of each image when we descend into the soil and the approximate height of our eye on the ground level (1.70 m) is what we call Myopia Space

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Kavzoglu, Taskin, and Merve Goral. "Google Earth Engine for Monitoring Marine Mucilage: Izmit Bay in Spring 2021." Hydrology 9, no. 8 (2022): 135. http://dx.doi.org/10.3390/hydrology9080135.

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Global warming together with environmental pollution threatens marine habitats and causes an increasing number of environmental disasters. Periodic monitoring of coastal water quality is of critical importance for the effective management of water resources and the sustainability of marine ecosystems. The use of remote sensing technologies provides significant benefits for detecting, monitoring, and analyzing rapidly occurring and displaced natural phenomena, including mucilage events. In this study, five water indices estimated from cloud-free and partly cloudy Sentinel-2 images acquired from

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Adewoye, Ralph, Peace Ukoha, and Stephen Okonkwo. "Mapping oil palm expansion within the protected lowland rainforest of Nigeria using Google Earth engine." PARKS, no. 29.2 (November 2023): 64–72. http://dx.doi.org/10.2305/vjsb2292.

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Increasing demand for Elaeis guineensis (African Oil Palm) products both for domestic and industrial use has led to its continuous expansion. The influence of oil palm plantation establishment on the economic well-being of communities and ecosystems cannot be over-emphasised. The study focuses on the rapid expansion of oil palm plantations within all protected areas and forest reserves in the lowland rainforests of Ondo State, Nigeria using. Object-Based Image Analysis (OBIA) was used to map oil palm expansion using 10-metre resolution Sentinel-2A images for 2015 and 2020 in Google Earth Engin

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Pan, Feifei, Xiaohuan Xi, and Cheng Wang. "A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery." Remote Sensing 12, no. 10 (2020): 1611. http://dx.doi.org/10.3390/rs12101611.

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A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blu

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Gentet, Philippe, Tam Le Phuc Do, Jumamurod Farhod Ugli Aralov, Oybek Mirzaevich Narzulloev, Leehwan Hwang, and Seunghyun Lee. "From Google Earth Studio to Hologram: A Pipeline for Architectural Visualization." Applied Sciences 15, no. 11 (2025): 6179. https://doi.org/10.3390/app15116179.

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High-resolution holographic visualization of built environments remains largely inaccessible due to the complexity and technical demands of traditional 3D data acquisition processes. This study proposes a workflow for producing high-quality full-color digital holographic stereograms of architectural landmarks using Google Earth Studio. By leveraging photogrammetrically reconstructed three-dimensional (3D) city models and a controlled camera path, we generated perspective image sequences of two iconic monuments, that is, the Basílica de la Sagrada Família (Barcelona, Spain) and the Arc de Triom

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Bennett, Mary K., Nicolas Younes, and Karen Joyce. "Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine." Drones 4, no. 3 (2020): 50. http://dx.doi.org/10.3390/drones4030050.

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While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft system

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Panchal, Dr V. K., Harish Kundra, and Gaganpreet Singh. "Lexical Analyser for Automated Image Classification and Applying Classification on Google Earth." International Journal of Computer Applications 1, no. 8 (2010): 99–101. http://dx.doi.org/10.5120/173-300.

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Xue, Hanyu, Xingang Xu, Qingzhen Zhu, et al. "Object-Oriented Crop Classification Using Time Series Sentinel Images from Google Earth Engine." Remote Sensing 15, no. 5 (2023): 1353. http://dx.doi.org/10.3390/rs15051353.

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The resulting maps of land use classification obtained by pixel-based methods often have salt-and-pepper noise, which usually shows a certain degree of cluttered distribution of classification image elements within the region. This paper carries out a study on crop classification and identification based on time series Sentinel images and object-oriented methods and takes the crop recognition and classification of the National Modern Agricultural Industrial Park in Jalaid Banner, Inner Mongolia, as the research object. It uses the Google Earth Engine (GEE) cloud platform to extract time series

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Tang, Zixia, Mengmeng Li, and Xiaoqin Wang. "Mapping Tea Plantations from VHR Images Using OBIA and Convolutional Neural Networks." Remote Sensing 12, no. 18 (2020): 2935. http://dx.doi.org/10.3390/rs12182935.

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Tea is an important economic plant, which is widely cultivated in many countries, particularly in China. Accurately mapping tea plantations is crucial in the operations, management, and supervision of the growth and development of the tea industry. We propose an object-based convolutional neural network (CNN) to extract tea plantations from very high resolution remote sensing images. Image segmentation was performed to obtain image objects, while a fine-tuned CNN model was used to extract deep image features. We conducted feature selection based on the Gini index to reduce the dimensionality o

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Trinh, Le Hung, and Van Phu Le. "Application of Sentinel 1 imagery data to detect and classify oil spills on the ocean." Petrovietnam Journal 2 (March 21, 2022): 32–38. http://dx.doi.org/10.47800/pvj.2022.02-05.

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Sentinel is the name of a series of Earth observation missions (from Sentinel 1 to Sentinel 6) developed by the Copernicus initiative and operated by the European Space Agency (ESA). Sentinel satellite image data, which includes optical and radar images, provided completely free of charge, has been widely and effectively used in Earth research. The paper presents a technical solution using Sentinel 1 satellite image in detecting and monitoring oil spill pollution at sea, testing for Mauritius sea area. The Otsu automatic thresholding method was applied to extract oil spills at sea from Sentine

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Sugema, Muarif, and Farlin Rosyad. "PEMANFAATAN CITRA SATELIT GOOGLE EARTH UNTUK PEMBUATAN PETA TATA GUNA LAHAN KECAMATAN KALIDONI." Ensiklopedia Research and Community Service Review 4, no. 2 (2025): 105–9. https://doi.org/10.33559/err.v4i2.2937.

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This study aims to analyze the effectiveness of Google Earth satellite imagery utilization in creating land use maps in Kalidoni District, Palembang. The research method uses a quantitative descriptive approach with remote sensing techniques and field surveys. Sampling was conducted at 150 points using stratified random sampling method. The results showed an interpretation accuracy level of 92.7% with a Kappa coefficient of 0.89. Land use distribution is dominated by settlements (35.2%), industrial areas (20.5%), and commercial services (15.3%). Temporal analysis for the 2019-2024 period shows

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Pu, D. C., J. Y. Sun, Q. Ding, Q. Zheng, T. T. Li, and X. F. Niu. "MAPPING URBAN AREAS USING DENSE TIME SERIES OF LANDSAT IMAGES AND GOOGLE EARTH ENGINE." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (February 7, 2020): 403–9. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-403-2020.

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Abstract. Urban information extraction from satellite based remote sensing data could provide the basic scientific decision-making data for the construction and management of future cities. In particular, long-term satellite based remote sensing such as Landsat observations provides a rich source of data for urban area mapping. Urban area mapping based on the single-temporal Landsat observations is vulnerable to data quality (such as cloud coverage and stripe), and it is difficult to extract urban areas accurately. The composite of dense time series Landsat observations can significantly reduc

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Hasan, Sajjad H., Amjed N. M. AL-Hameedawi, and H. S. Ismael. "Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate." IOP Conference Series: Earth and Environmental Science 961, no. 1 (2022): 012051. http://dx.doi.org/10.1088/1755-1315/961/1/012051.

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Abstract As a result of the advancements that have occurred in the technical field of geomatics, particularly after the development of developmental programming environments, they have become the most important machine for conducting image analyses of satellite data, creating and modifying spatial analysis tools, and performing large data analyses at a fast rate without the need for high-end specifications on the personal computer. This study has several objectives, including the definition and popularization of the use of the power of Google Earth Engine (GEE) in the speed of conducting spati

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