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Anthony C. Robinson Scott Pezanowski Sarah Troedson Raechel Bianchetti Justine Blanford Joshua Stevens 《制图学和地理信息科学》2013,40(5):415-426
Maps are a primary means for supporting information sharing and collaboration in emergency management and crisis situations. While a variety of formalized map symbol standards for emergency contexts exist, they have not been widely adopted by mapmakers. Informal symbol conventions are commonly used within emergency management stakeholder groups, but until now there has not been a flexible mechanism for discovering, sharing, and previewing these symbol sets among mapmakers. In this paper, we describe the design and development of the Symbol Store, a visually enabled, web-based interactive tool intended to help mapmakers share point symbols. The Symbol Store allows users to browse symbols by keyword, category tags, and contributors. It also allows for symbols to be previewed on realistic maps prior to download. An initial prototype of the Symbol Store was evaluated by flood mapping experts from the State of California, and the results of this user study led to multiple refinements now implemented in the public version of Symbol Store located at www.symbolstore.org. 相似文献
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Saeed Ojaghi Farshid Farnood Ahmadi Hamid Ebadi Raechel Bianchetti 《Arabian Journal of Geosciences》2017,10(21):470
Lake Urmia, located in northwest Iran, contains a number of wetlands significantly affecting the environmental, social, and economic conditions of the region. The ecological condition of Lake Urmia has degraded during the past decade, due to climate change, human activities, and unsustainable management. The poor condition of the lake has also affected the surrounding wetlands. This study analyzes the land cover change of one of the wetlands in the southern part of Lake Urmia, known as Ghara-Gheshlagh wetland, in the period 1989–2015 using post-classification change detection and machine learning image classification. For this analysis, three Landsat images, acquired in 1989 (TM), 2001 (TM), and 2015 (Landsat-8), were used for the classification and change detection. Support vector machine learning algorithm, a supervised learning method, is employed, and images are classified into four main land cover classes namely “water,” ”barren,” “salty land,” and “agriculture and grassland.” Change detection was carried out for pairs of years 1989 to 2001 and 2001 until 2015. The results of this classification show that there is a sharp increase in the area of salt-saturated land as well as a decrease in the area of water resources. Overall classification accuracy obtained were high for the individual years: 1989 (91.48%), 2001 (90.63%), and 2015 (88.6%). Also, the Kappa coefficients for individual maps were high: 1989 (0.89), 2001 (0.8742), and 2015 (0.84). After that, the land cover change map of the study area is obtained between 1989 to 2001 and then 2001 to 2015. The results of this analysis suggest that more efforts should be taken to effectively manage water resources in the region and point to potential locations for focused management actions within the wetland area. 相似文献
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Hoffimann J&#;lio Augusto Jos&#; Resende Lucas Mathias Marlon Mazzinghy Douglas Bianchetti Matheus Mendes M&#;nica Souza Thiago Andrade Vitor Domingues Tarc&#;sio Silva Wesley Silva Ruberlan Couto Danielly Fonseca Elisabeth Gon&#;alves Keila 《Mathematical Geosciences》2022,54(7):1227-1253
Mathematical Geosciences - In mine planning, geospatial estimates of variables such as comminution indexes and metallurgical recovery are extremely important to locate blocks for which the energy... 相似文献
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