scholarly journals AN AUTOMATIC UAV MAPPING SYSTEM FOR SUPPORTING UN (UNITED NATIONS) FIELD OPERATIONS

Author(s):  
K. Choi ◽  
J. W. Cheon ◽  
H. Y. Kim ◽  
I. Lee

The United Nations (UN) has performed field operations worldwide such as peacekeeping or rescue missions. When such an operation is needed, the UN dispatches an operation team usually with a GIS (Geographic Information System) customized to a specific operation. The base maps for the GIS are generated mostly with satellite images which may not retain a high resolution and the current situation. To build an up-to-date high resolution map, we propose a UAV (unmanned aerial vehicle) based automatic mapping system, which can operate in a fully automatic way from the data acquisition of sensory data to the data processing for the generation of the geospatial products such as a mosaicked orthoimage of a target area. In this study, we analyse the requirements for UN field operations, suggest a UAV mapping system with an operation scenario, and investigate the applicability of the system. With the proposed system, we can construct a tailored GIS with up-to-date and high resolution base maps for a specific operation efficiently.

Author(s):  
K. Choi ◽  
J. W. Cheon ◽  
H. Y. Kim ◽  
I. Lee

The United Nations (UN) has performed field operations worldwide such as peacekeeping or rescue missions. When such an operation is needed, the UN dispatches an operation team usually with a GIS (Geographic Information System) customized to a specific operation. The base maps for the GIS are generated mostly with satellite images which may not retain a high resolution and the current situation. To build an up-to-date high resolution map, we propose a UAV (unmanned aerial vehicle) based automatic mapping system, which can operate in a fully automatic way from the data acquisition of sensory data to the data processing for the generation of the geospatial products such as a mosaicked orthoimage of a target area. In this study, we analyse the requirements for UN field operations, suggest a UAV mapping system with an operation scenario, and investigate the applicability of the system. With the proposed system, we can construct a tailored GIS with up-to-date and high resolution base maps for a specific operation efficiently.


2019 ◽  
Author(s):  
Kersten Bergstrom ◽  
Austin B. Lawrence ◽  
Alex J. Pelissero ◽  
Lauren J. Hammond ◽  
Eliwasa Maro ◽  
...  

Isimila is a Middle Pleistocene archaeological site located in southern Tanzania. The site is known for large surface assemblages of later Acheulean lithics such as hand axes, cleavers, scrapers, and cores. While hominin remains have yet to be discovered at the site, Isimila offers a unique window into Middle Pleistocene Homo behavior. Although Isimila has been studied extensively, the last published map of the site and surrounding area was made available in the 1970s. Here, we present an updated high-resolution map of Isimila. Data for the map were collected during aerial survey with an uncrewed/unmanned aerial vehicle (UAV). With this map, we identify new archaeological localities, erosional patterns, newly exposed geological features, and changes in site topography. The map reveals patterns of stone tool and raw material distribution that may support previous hypotheses of raw material transport into the area by hominins. This open-access map establishes a baseline for tracking changes to site topography in the future and serves as a unique tool to enable collaboration between researchers, museum personnel, and local populations to better conserve Isimila.


2021 ◽  
Vol 93 (3) ◽  
Author(s):  
JULIO A. MOREIRA ◽  
FABRICIA B. DE OLIVEIRA ◽  
CARLOS H.R. DE OLIVEIRA ◽  
ALVARO C. FIGUEIREDO ◽  
MAURO C.L. FILHO ◽  
...  

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.


Author(s):  
А.С. Алексеев ◽  
А.А. Никифоров ◽  
А.А. Михайлова ◽  
М.Р. Вагизов

В связи со старением информационных материалов о состоянии лесов существует потребность в разработке новых методов таксации древостоев, основанных на применении последних научно-технических достижений в области теории структуры и продуктивности древостоев, дистанционных методов изучения лесов, информационных и ГИС технологий. В статье приведены результаты разработки и проверки нового метода определения таксационных характеристик сомкнутых насаждений на основе правила 3/2 и подобных ему правил Хильми и Рейнеке, с одной стороны, и определения числа деревьев на единице площади по снимку сверх высокого разрешения, полученного с помощью БПЛА, с другой. С теоретической точки зрения эта зависимости величин запаса, средней высоты и среднего диаметра от числа стволов на единице площади относятся к классу аллометрических связей, очень часто встречающихся при количественном описании соотношений частей биологических систем разных уровней иерархии, от организмов до экосистем. Параметры аллометрических зависимостей запаса, средних высоты и диаметра от числа стволов на единице площади были определены для основных лесообразующих пород по данным таблиц хода роста нормальных (полных) древостоев с теоретическим показателем степени и затем использованы для расчетов. Число деревьев на единице площади определялось по снимку с разрешением 7,13 см/пиксель, полученному с помощью 4-роторной платформы. Обработка материалов аэрофотосъемки была выполнена в специализированной фотограмметрической системе Agisoft Photoscan. В результате были получены ортофотоплан и цифровая модель поверхности крон деревьев на изучаемую территорию с определением их высот. Для автоматизированной обработки полученных изображений с целью получения значений числа деревьев на единицу площади был создан специализированный скрипт на языке Java. Погрешности определения таксационных характеристик древостоев предлагаемым методом не выше установленных действующими нормативными материалами. Every time there is a demand for new innovative methods of forest resources estimation based on last achievements in theoretical science, remote sensing methods, information and GIS-technologies. In the paper are presented a new method and the results of its application to forest stands growing stock, mean height and diameter determination. The method is based on rule 3/2 and similar Reineke and Hilmy rules, on one hand and high resolution image made by unmanned aerial vehicle, which used for determination of number of trees per area unit, on other. The above rules are well known in quantitative biology as an allometric and widely used for description of different kind of relations in biological systems of various scale: from organisms to ecosystems. Parameters of above allometric relationships between growing stock, mean height and diameter and stems density per area unit was determine on the base of full stock growth and yield tables for main tree species and after used for experimental calculations. The number of trees per area unit was determined after special treatment of high resolution image made by unmanned flying machine. The growing stock, mean height and diameter determined by suggested method was compared with the data of regular forest inventory. Comparison gives positive result and method may be recommended for further development.


2021 ◽  
pp. 1-11
Author(s):  
Yasser Mostafa ◽  
Mahmoud Nokrashy O. Ali ◽  
Faten Mostafa ◽  
Mohamed Yousef

Forests ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 66
Author(s):  
Kirill A. Korznikov ◽  
Dmitry E. Kislov ◽  
Jan Altman ◽  
Jiří Doležal ◽  
Anna S. Vozmishcheva ◽  
...  

Very high resolution satellite imageries provide an excellent foundation for precise mapping of plant communities and even single plants. We aim to perform individual tree recognition on the basis of very high resolution RGB (red, green, blue) satellite images using deep learning approaches for northern temperate mixed forests in the Primorsky Region of the Russian Far East. We used a pansharpened satellite RGB image by GeoEye-1 with a spatial resolution of 0.46 m/pixel, obtained in late April 2019. We parametrized the standard U-Net convolutional neural network (CNN) and trained it in manually delineated satellite images to solve the satellite image segmentation problem. For comparison purposes, we also applied standard pixel-based classification algorithms, such as random forest, k-nearest neighbor classifier, naive Bayes classifier, and quadratic discrimination. Pattern-specific features based on grey level co-occurrence matrices (GLCM) were computed to improve the recognition ability of standard machine learning methods. The U-Net-like CNN allowed us to obtain precise recognition of Mongolian poplar (Populus suaveolens Fisch. ex Loudon s.l.) and evergreen coniferous trees (Abies holophylla Maxim., Pinus koraiensis Siebold & Zucc.). We were able to distinguish species belonging to either poplar or coniferous groups but were unable to separate species within the same group (i.e. A. holophylla and P. koraiensis were not distinguishable). The accuracy of recognition was estimated by several metrics and exceeded values obtained for standard machine learning approaches. In contrast to pixel-based recognition algorithms, the U-Net-like CNN does not lead to an increase in false-positive decisions when facing green-colored objects that are similar to trees. By means of U-Net-like CNN, we obtained a mean accuracy score of up to 0.96 in our computational experiments. The U-Net-like CNN recognizes tree crowns not as a set of pixels with known RGB intensities but as spatial objects with a specific geometry and pattern. This CNN’s specific feature excludes misclassifications related to objects of similar colors as objects of interest. We highlight that utilization of satellite images obtained within the suitable phenological season is of high importance for successful tree recognition. The suitability of the phenological season is conceptualized as a group of conditions providing highlighting objects of interest over other components of vegetation cover. In our case, the use of satellite images captured in mid-spring allowed us to recognize evergreen fir and pine trees as the first class of objects (“conifers”) and poplars as the second class, which were in a leafless state among other deciduous tree species.


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