We are proudly a Ukrainian website. Our country was attacked by Russian Armed Forces on Feb. 24, 2022.
You can support the Ukrainian Army by following the link: https://u24.gov.ua/.
Even the smallest donation is hugely appreciated!
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Divine Road of the Earth".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Divine Road of the Earth":
1
Li, Qiuping, Haowen Luo, and Xuechen Luan. "Multistage Impacts of the Heavy Rain Process on the Travel Speeds of Urban Roads." ISPRS International Journal of Geo-Information 10, no. 8 (August 17, 2021): 557. http://dx.doi.org/10.3390/ijgi10080557.
Heavy rain causes the highest drop in travel speeds compared with light and moderate rain because it can easily induce flooding on road surfaces, which can continue to hinder urban transportation even after the rainfall is over. However, very few studies have specialized in researching the multistage impacts of the heavy rain process on urban roads, and the cumulative effects of heavy rain in road networks are often overlooked. In this study, the heavy rain process is divided into three consecutive stages, i.e., prepeak, peak, and postpeak. The impact of heavy rain on a road is represented by a three-dimensional traffic speed change ratio vector. Then, the k-means clustering method is implemented to reveal the distinct patterns of speed change ratio vectors. Finally, the characteristics of the links in each cluster are analyzed. An empirical study of Shenzhen, China suggests that there are three major impact patterns in links. The differences among links associated with the three impact patterns are related to the road category, travel speeds in no rain days, and the number of transportation facilities. The findings in this research can contribute to a more in-depth understanding of the relationship between the heavy rain process and the travel speeds of urban roads and provide valuable information for traffic management and personal travel in heavy rain weather.
2
Hu, Anna, Siqiong Chen, Liang Wu, Zhong Xie, Qinjun Qiu, and Yongyang Xu. "WSGAN: An Improved Generative Adversarial Network for Remote Sensing Image Road Network Extraction by Weakly Supervised Processing." Remote Sensing 13, no. 13 (June 26, 2021): 2506. http://dx.doi.org/10.3390/rs13132506.
Road networks play an important role in navigation and city planning. However, current methods mainly adopt the supervised strategy that needs paired remote sensing images and segmentation images. These data requirements are difficult to achieve. The pair segmentation images are not easy to prepare. Thus, to alleviate the burden of acquiring large quantities of training images, this study designed an improved generative adversarial network to extract road networks through a weakly supervised process named WSGAN. The proposed method is divided into two steps: generating the mapping image and post-processing the binary image. During the generation of the mapping image, unlike other road extraction methods, this method overcomes the limitations of manually annotated segmentation images and uses mapping images that can be easily obtained from public data sets. The residual network block and Wasserstein generative adversarial network with gradient penalty loss were used in the mapping network to improve the retention of high-frequency information. In the binary image post-processing, this study used the dilation and erosion method to remove salt-and-pepper noise and obtain more accurate results. By comparing the generated road network results, the Intersection over Union scores reached 0.84, the detection accuracy of this method reached 97.83%, the precision reached 92.00%, and the recall rate reached 91.67%. The experiments used a public dataset from Google Earth screenshots. Benefiting from the powerful prediction ability of GAN, the experiments show that the proposed method performs well at extracting road networks from remote sensing images, even if the roads are covered by the shadows of buildings or trees.
3
Ayala, Christian, Rubén Sesma, Carlos Aranda, and Mikel Galar. "A Deep Learning Approach to an Enhanced Building Footprint and Road Detection in High-Resolution Satellite Imagery." Remote Sensing 13, no. 16 (August 7, 2021): 3135. http://dx.doi.org/10.3390/rs13163135.
The detection of building footprints and road networks has many useful applications including the monitoring of urban development, real-time navigation, etc. Taking into account that a great deal of human attention is required by these remote sensing tasks, a lot of effort has been made to automate them. However, the vast majority of the approaches rely on very high-resolution satellite imagery (<2.5 m) whose costs are not yet affordable for maintaining up-to-date maps. Working with the limited spatial resolution provided by high-resolution satellite imagery such as Sentinel-1 and Sentinel-2 (10 m) makes it hard to detect buildings and roads, since these labels may coexist within the same pixel. This paper focuses on this problem and presents a novel methodology capable of detecting building and roads with sub-pixel width by increasing the resolution of the output masks. This methodology consists of fusing Sentinel-1 and Sentinel-2 data (at 10 m) together with OpenStreetMap to train deep learning models for building and road detection at 2.5 m. This becomes possible thanks to the usage of OpenStreetMap vector data, which can be rasterized to any desired resolution. Accordingly, a few simple yet effective modifications of the U-Net architecture are proposed to not only semantically segment the input image, but also to learn how to enhance the resolution of the output masks. As a result, generated mappings quadruplicate the input spatial resolution, closing the gap between satellite and aerial imagery for building and road detection. To properly evaluate the generalization capabilities of the proposed methodology, a data-set composed of 44 cities across the Spanish territory have been considered and divided into training and testing cities. Both quantitative and qualitative results show that high-resolution satellite imagery can be used for sub-pixel width building and road detection following the proper methodology.
4
Stewart, Christopher, Michele Lazzarini, Adrian Luna, and Sergio Albani. "Deep Learning with Open Data for Desert Road Mapping." Remote Sensing 12, no. 14 (July 15, 2020): 2274. http://dx.doi.org/10.3390/rs12142274.
The availability of free and open data from Earth observation programmes such as Copernicus, and from collaborative projects such as Open Street Map (OSM), enables low cost artificial intelligence (AI) based monitoring applications. This creates opportunities, particularly in developing countries with scarce economic resources, for large–scale monitoring in remote regions. A significant portion of Earth’s surface comprises desert dune fields, where shifting sand affects infrastructure and hinders movement. A robust, cost–effective and scalable methodology is proposed for road detection and monitoring in regions covered by desert sand. The technique uses Copernicus Sentinel–1 synthetic aperture radar (SAR) satellite data as an input to a deep learning model based on the U–Net architecture for image segmentation. OSM data is used for model training. The method comprises two steps: The first involves processing time series of Sentinel–1 SAR interferometric wide swath (IW) acquisitions in the same geometry to produce multitemporal backscatter and coherence averages. These are divided into patches and matched with masks of OSM roads to form the training data, the quantity of which is increased through data augmentation. The second step includes the U–Net deep learning workflow. The methodology has been applied to three different dune fields in Africa and Asia. A performance evaluation through the calculation of the Jaccard similarity coefficient was carried out for each area, and ranges from 84% to 89% for the best available input. The rank distance, calculated from the completeness and correctness percentages, was also calculated and ranged from 75% to 80%. Over all areas there are more missed detections than false positives. In some cases, this was due to mixed infrastructure in the same resolution cell of the input SAR data. Drift sand and dune migration covering infrastructure is a concern in many desert regions, and broken segments in the resulting road detections are sometimes due to sand burial. The results also show that, in most cases, the Sentinel–1 vertical transmit–vertical receive (VV) backscatter averages alone constitute the best input to the U–Net model. The detection and monitoring of roads in desert areas are key concerns, particularly given a growing population increasingly on the move.
5
., Surnata, Bambang Setiawan, Purboyo ., and Yuni Karlina. "Case Study of Damaged Road Surfaces as a Result of Drainage on Sabar Jaya Road Kelurahan Mariana ILIR." Volume 5 - 2020, Issue 8 - August 5, no. 8 (September 4, 2020): 1042–47. http://dx.doi.org/10.38124/ijisrt20aug363.
Banyuasin Regency in addition to having a strategic geographical location that is located in the inter-provincial route also has abundant natural resources. Geographical Location Banyuasin Regency is located in a position between 1.30 ° - 4.0 ° South Latitude and 104 ° 00 '- 105 ° 35' East Longitude that starts from the central part of the Province of South Sumatra to the East. Banyuasin Regency has an area of 12,431 km² and is divided into 19 districts, one of which is Banyuasin I. The Jalan Pati jaya is Jalan Kecamatan, which is located in the Sub-district of Mariana ilir, Subdistrict of Banyuasin I. This road only connects the Mariana sub-district to the village of Prajin. the current condition of the road has been intersected with the proboscis river where the condition of the road has been damaged due to inundation of rain water, in addition to the inundation of the soil the quality is still unstable and the road surface is also thin. And dranase channels are currently in a mapet state. On the subject matter above the author only examines and analyzes three elements, among others. The aspect of Hydrology is the science relating to water on earth, both regarding its occurrence, circulation and distribution, its properties and its relationship with the environment, especially with living things. The large number of parameters makes hydrological analysis difficult to solve analytically. Besides that hydrological conditions depend on changes / activities carried out by humans such as changes in land use. (Triatmodjo, 2008 h 1)
6
Cira, Calimanut-Ionut, Ramon Alcarria, Miguel-Ángel Manso-Callejo, and Francisco Serradilla. "A Framework Based on Nesting of Convolutional Neural Networks to Classify Secondary Roads in High Resolution Aerial Orthoimages." Remote Sensing 12, no. 5 (February 27, 2020): 765. http://dx.doi.org/10.3390/rs12050765.
Remote sensing imagery combined with deep learning strategies is often regarded as an ideal solution for interpreting scenes and monitoring infrastructures with remarkable performance levels. In addition, the road network plays an important part in transportation, and currently one of the main related challenges is detecting and monitoring the occurring changes in order to update the existent cartography. This task is challenging due to the nature of the object (continuous and often with no clearly defined borders) and the nature of remotely sensed images (noise, obstructions). In this paper, we propose a novel framework based on convolutional neural networks (CNNs) to classify secondary roads in high-resolution aerial orthoimages divided in tiles of 256 × 256 pixels. We will evaluate the framework’s performance on unseen test data and compare the results with those obtained by other popular CNNs trained from scratch.
7
Kwiecień, Janusz, and Kinga Szopińska. "Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City." Remote Sensing 12, no. 18 (September 6, 2020): 2885. http://dx.doi.org/10.3390/rs12182885.
Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.
8
Zong, Leli, Sijia He, Jiting Lian, Qiang Bie, Xiaoyun Wang, Jingru Dong, and Yaowen Xie. "Detailed Mapping of Urban Land Use Based on Multi-Source Data: A Case Study of Lanzhou." Remote Sensing 12, no. 12 (June 20, 2020): 1987. http://dx.doi.org/10.3390/rs12121987.
Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use.
9
Stanzel, Michael, and Jon Preston-Thomas. "OECD DIVINE Project: road simulator testing." International Journal of Heavy Vehicle Systems 7, no. 1 (2000): 34. http://dx.doi.org/10.1504/ijhvs.2000.004518.
Rahmati, Omid, Saleh Yousefi, Zahra Kalantari, Evelyn Uuemaa, Teimur Teimurian, Saskia Keesstra, Tien Pham, and Dieu Tien Bui. "Multi-Hazard Exposure Mapping Using Machine Learning Techniques: A Case Study from Iran." Remote Sensing 11, no. 16 (August 20, 2019): 1943. http://dx.doi.org/10.3390/rs11161943.
Mountainous areas are highly prone to a variety of nature-triggered disasters, which often cause disabling harm, death, destruction, and damage. In this work, an attempt was made to develop an accurate multi-hazard exposure map for a mountainous area (Asara watershed, Iran), based on state-of-the art machine learning techniques. Hazard modeling for avalanches, rockfalls, and floods was performed using three state-of-the-art models—support vector machine (SVM), boosted regression tree (BRT), and generalized additive model (GAM). Topo-hydrological and geo-environmental factors were used as predictors in the models. A flood dataset (n = 133 flood events) was applied, which had been prepared using Sentinel-1-based processing and ground-based information. In addition, snow avalanche (n = 58) and rockfall (n = 101) data sets were used. The data set of each hazard type was randomly divided to two groups: Training (70%) and validation (30%). Model performance was evaluated by the true skill score (TSS) and the area under receiver operating characteristic curve (AUC) criteria. Using an exposure map, the multi-hazard map was converted into a multi-hazard exposure map. According to both validation methods, the SVM model showed the highest accuracy for avalanches (AUC = 92.4%, TSS = 0.72) and rockfalls (AUC = 93.7%, TSS = 0.81), while BRT demonstrated the best performance for flood hazards (AUC = 94.2%, TSS = 0.80). Overall, multi-hazard exposure modeling revealed that valleys and areas close to the Chalous Road, one of the most important roads in Iran, were associated with high and very high levels of risk. The proposed multi-hazard exposure framework can be helpful in supporting decision making on mountain social-ecological systems facing multiple hazards.
Churchill, Timothy William Ralph. "Divine initiative and the Christology of the Damascus Road Encounter." Thesis, Brunel University, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.509842.
Hundley, Michael Bing. "Keeping heaven on earth : safeguarding the divine presence in the Priestly tabernacle." Thesis, University of Cambridge, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.608479.
Magallanes, Sophia Ann. "Bringing wisdom back down to earth : a wisdom reading of Job 28." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5466.
This thesis aims to do what the poem Job 28 is trying to do in the Book of Job, which is to focus on prescribed biblical wisdom practice in order to ‘bring wisdom back down to earth’ within a discussion concerning divine justice (Job 22-31). Chapter 1 introduces what a “wisdom reading” is and why it is necessary. Chapters 2-5 of this thesis give a close reading of Job 28:1-28 and includes an intentional dialogue between how the words, phrase, and theological concepts are used in the poem and in the main three bible wisdom texts (Job, Proverbs and Qoheleth). Chapter 6 discusses the implications of reading Job 28 in light of its biblical wisdom tradition. Job 28 speaks of a hidden wisdom, but it is not obvious how this prescribed wisdom (“fear of God and avoiding evil”) is connected to divine justice until the poem is read within the of context of the three main biblical wisdom books (Job, Proverbs, Qoheleth). A close reading of Job 28:1-1 and 12-28 within the context of the biblical wisdom tradition, challenges the reader to redefine what the book of Job is saying about wisdom in ethical terms and, therefore, also provokes a redefinition of the divine gaze upon the earth in terms of divine justice. In this thesis, we shall see how wisdom and divine justice are both rooted in earthly matters. It is only when viewed as “down-to-earth” matters that we see that they are related to each other in sapiential literature, especially in Job 28. If ‘wisdom’ is understood as proper conduct on earth (avoiding evil action, Job 28:28b) prompted by an understanding that God gazes on this earth he created (fear of the Lord, Job 28:28a), then divine justice is to be understood as divine regulation of that proper conduct and attitude.
4
Kutsko, John F. "Between heaven and earth : divine presence and absence in the Book of Ezekiel /." Winona Lake (Ind.) : Eisenbrauns, 2000. http://catalogue.bnf.fr/ark:/12148/cb388395570.
Cormac McCarthy's The Road and Dante's the Inferno contain textual and thematic comparisons. While the Inferno creates a world that exhibits the worst fears of the medieval Catholic subconscious of Dante's time, The Road paints a world of the darkest fears of the current American subconscious. Both texts reflect a critical dystopia that speculates on human spirituality and offers a critique of society through a tour of sin and suffering in a desolate setting.
6
Pyle, Jesse Colton. "“The Planet that Leads Men Straight on Every Road:” The Sun, Salvation, and Spiritual Allegory in Dante’s Commedia." Ohio University Honors Tutorial College / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1307630062.
Cheung, Kwong-chung. "Reinforced earth wall design & construction in northern access road for Cyberport Development /." View the Table of Contents & Abstract, 2005. http://sunzi.lib.hku.hk/hkuto/record/B3676288X.
Cheung, Kwong-chung, and 張光中. "Reinforced earth wall design & construction in northern access road for Cyberport Development." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2005. http://hub.hku.hk/bib/B45014279.
Zhang, Ruibo, and Manni Chen. "Extraction of street from google earth imagery." Thesis, Högskolan i Gävle, Avdelningen för Industriell utveckling, IT och Samhällsbyggnad, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-9399.
Extraction of streets from Google earth imagery is a hot research topic. The main purpose of this paper is to create a method to extract streets information from satellite image automatically. It is exceedingly difficult to achieve, because every road has different characters and there are a lot of noises (e.g. shadow, building, and vehicle) in the image. By using generic color model and the image analysis techniques, we build up the automatic road extraction system. It extracted road successfully from mid-size city image with a very high extraction rate. Some interesting discoveries and unique creative solution are proposed in this paper.
This research programme is concerned with the design of road traffic noise barriers, in particular, the use of multiple-walls on the ground and on top of earth mound type barriers. As part of this research, a comprehensive up-to-date review of the research carried out on noise barriers was undertaken. A number of areas requiring further research were identified. The discussion of these resulted in the proposal of a simplified noise barrier selection method which would be of use particularly to non-acousticians. This method indicated that acoustic information available for the design of earth mounds was limited, although this barrier type is commonly used in practice and is known to have a number of non-acoustic benefits. Initial investigations showed that the performance of an earth mound could be enhanced by the use of multiple-walls on its top. A detailed investigation was undertaken into the acoustic performance of multiple-walls both on the ground and on top of earth mounds. Both physical and numerical modelling techniques were used for this purpose. The physical scale modelling experiments were carried out both under uniform field conditions and in two different semi-anechoic chambers in the presence of a continuous noise source, using a model scale of 1: 10. The numerical modelling was applied using indirect boundary element method formulation. The commercial software named SYSNOISE was employed for the computations. It was found that numerical modelling results and the semi-anechoic chamber experiments generally agreed very well. The level of accuracy of the uniform field experiments depended on the choice of source and receiver locations as well as the size of the model geometry. This investigation resulted in acoustic advice on the use of multiple walls both on their own and on top of earth mounds. Under favourable conditions, the multiple-wall configurations were shown to provide substantial attenuations of up to 26dB. The physical parameters involved in their design and their noise attenuation mechanisms were identified. In addition to long-wave scattering and diffraction effects, it was identified that surface wave generation mechanisms and interference effects played a role in attenuating noise. The acoustic advice for the design of earth mounds was extended to the applications of single, double and multiple-walls on their top. This work also showed that uniform field conditions in conjunction with a continuous noise source could be used for physical modelling. It was found that for small-sized geometries good agreements were observed between physical modelling (both types) and numerical simulations. There were lesser agreements between the sets of data for larger geometries. The multiple-wall configurations investigated as part of this research programme could be used as noise mitigating measures in central reservations of dual carriageways. However, further research would be required into their acoustic performance and engineering design. The results obtained from this investigation have led to the identification of a number of research areas which could be undertaken in the future.
(Canada), National Round Table on the Environment and the Economy. On the road to Brazil: The Earth Summit. Ottawa: National Round Table on the Environment and the Economy, 1991.
Sha, Zhi Gang. Divine soul mind body healing and transmission systems: The divine way to heal you, humanity, Mother Earth. London: Simon & Schuster, 2011.
Mayor, Michel, Christophe Lovis, Francesco Pepe, Damien Ségransan, and Stèphane Udry. "The Road to Earth Twins." In Reviews in Modern Astronomy, 1–10. Weinheim, Germany: Wiley-VCH Verlag GmbH & Co. KGaA, 2012. http://dx.doi.org/10.1002/9783527644384.ch1.
Katch, Rachel K., Christopher W. Myers, Francis G. O’Connor, and Douglas J. Casa. "A Road Map for Interdisciplinary Collaborations." In SpringerBriefs in Medical Earth Sciences, 101–12. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75889-3_8.
Udry, S., F. Pepe, C. Lovis, and M. Mayor. "Exoplanets: The Road to Earth Twins." In Astrophysics and Space Science Proceedings, 155–62. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9190-2_26.
Raghuram, G., Samantha Bastian, and Satyam Shivam Sundaram. "Megaprojects in India: Environmental and Land Acquisition Issues in the Road Sector." In Engineering Earth, 601–15. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9920-4_34.
Nelson, Frederick E. "“America’s Glory Road” … On Ice: Permafrost and the Development of the Alcan Highway, 1942–1943." In Engineering Earth, 643–61. Dordrecht: Springer Netherlands, 2010. http://dx.doi.org/10.1007/978-90-481-9920-4_37.
Verma, Surendra P. "Basic Concepts of Geochemistry and Composition of Earth Materials." In Road from Geochemistry to Geochemometrics, 1–158. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-9278-8_1.
Lillo, Julia Carabias. "Challenges and Road Blocks for Local and Global Sustainability." In Challenges of a Changing Earth, 193–95. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-19016-2_36.
Gaude-Ferragu, Murielle. "Chapter 8: The Road to Eternity: Devotions and the Divine." In Queenship in Medieval France, 1300-1500, 169–85. New York: Palgrave Macmillan US, 2016. http://dx.doi.org/10.1057/978-1-349-93028-9_9.
Terentiev, E. N., I. N. Prikhodko, I. I. Farshakova, I. D. Kuznetsov, and N. E. Shilin-Terentiev. "Localization of the Vortices and Road Sings in Images." In Springer Proceedings in Earth and Environmental Sciences, 303–16. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11533-3_30.
Steinberg, Richard N. "The Sun Goes Around the Earth." In An Inquiry into Science Education, Where the Rubber Meets the Road, 3–15. Rotterdam: SensePublishers, 2011. http://dx.doi.org/10.1007/978-94-6091-690-8_1.
Тези доповідей конференцій з теми "Divine Road of the Earth":
1
Nie, Yixiang, Richard B. Gomez, Menas Kafatos, and Ruixin Yang. "Hyperspectral imaging in earth road construction planning." In Aerospace/Defense Sensing, Simulation, and Controls, edited by William E. Roper. SPIE, 2001. http://dx.doi.org/10.1117/12.428246.
He, Jing, Hong Zhang, Tian Lan, Weiwei Cao, and Xun Wu. "Exploring the hierarchical structure in road network." In International Conference on Intelligent Earth Observing and Applications, edited by Guoqing Zhou and Chuanli Kang. SPIE, 2015. http://dx.doi.org/10.1117/12.2207827.
Borisova, Denitsa, and Valentina Hristova. "Experimental study on segmentation methods in road recognition." In Earth Resources and Environmental Remote Sensing/GIS Applications, edited by Ulrich Michel and Karsten Schulz. SPIE, 2018. http://dx.doi.org/10.1117/12.2327133.
Xu, Gang, Dawei Zhang, and Xinyu Liu. "Road extraction in high resolution images from Google Earth." In Signal Processing (ICICS). IEEE, 2009. http://dx.doi.org/10.1109/icics.2009.5397470.
Ni, Cui, Zequn Guan, and Qin Ye. "A new method of road extraction from high-resolution remote sensing imagery." In The Sixth International Symposium on Digital Earth, edited by Huadong Guo and Changlin Wang. SPIE, 2009. http://dx.doi.org/10.1117/12.872963.
Zheng, Zezhong, Chengjun Pu, Mingcang Zhu, Jun Xia, Xiang Zhang, Yalan Liu, and Jiang Li. "Damaged road extracting with high-resolution aerial image of post-earthquake." In International Conference on Intelligent Earth Observing and Applications, edited by Guoqing Zhou and Chuanli Kang. SPIE, 2015. http://dx.doi.org/10.1117/12.2207415.
Wang, Ximing, Hongrui Zhao, Baojun Fang, Gang Fu, and Wei Wang. "The development of road information extraction from remote sensing images." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815930.
Wu, Xun, Hong Zhang, Tian Lan, Weiwei Cao, and Jing He. "A quantitative approach to measure road network information based on edge diversity." In International Conference on Intelligent Earth Observing and Applications, edited by Guoqing Zhou and Chuanli Kang. SPIE, 2015. http://dx.doi.org/10.1117/12.2207585.
Wu, Mengze, Hui Jin, Bo Tang, Lei Yang, Tian Yang, and Jianwei Gong. "Constructing Topological Road Network of Wild Environment using Google Earth Pro." In 2019 IEEE Intelligent Transportation Systems Conference - ITSC. IEEE, 2019. http://dx.doi.org/10.1109/itsc.2019.8917090.
Звіти організацій з теми "Divine Road of the Earth":
1
Perret, D., R. Mompin, F. Bosse, and D. Demers. Stop 2-5B: Binette road earth flow induced by the June 23, 2010, Val-des-Bois earthquake. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2011. http://dx.doi.org/10.4095/289575.
Blundell, S. Micro-terrain and canopy feature extraction by breakline and differencing analysis of gridded elevation models : identifying terrain model discontinuities with application to off-road mobility modeling. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40185.
Elevation models derived from high-resolution airborne lidar scanners provide an added dimension for identification and extraction of micro-terrain features characterized by topographic discontinuities or breaklines. Gridded digital surface models created from first-return lidar pulses are often combined with lidar-derived bare-earth models to extract vegetation features by model differencing. However, vegetative canopy can also be extracted from the digital surface model alone through breakline analysis by taking advantage of the fine-scale changes in slope that are detectable in high-resolution elevation models of canopy. The identification and mapping of canopy cover and micro-terrain features in areas of sparse vegetation is demonstrated with an elevation model for a region of western Montana, using algorithms for breaklines, elevation differencing, slope, terrain ruggedness, and breakline gradient direction. These algorithms were created at the U.S. Army Engineer Research Center – Geospatial Research Laboratory (ERDC-GRL) and can be accessed through an in-house tool constructed in the ENVI/IDL environment. After breakline processing, products from these algorithms are brought into a Geographic Information System as analytical layers and applied to a mobility routing model, demonstrating the effect of breaklines as obstacles in the calculation of optimal, off-road routes. Elevation model breakline analysis can serve as significant added value to micro-terrain feature and canopy mapping, obstacle identification, and route planning.
