Page 66 - 2018 Annual Report Council of Agriculture, Executive Yuan

Collective Innovation Sustainable Management Unit 2: Building Agricultural Models B. Building a cooperative platform for agricultural spatial information In order to integrate geographic data sources and strengthen the effectiveness of applying this data in agricultural spatial management, the COA built a cooperative framework for agricultural geographic information systems (GIS), including the following: (a) Establishing shared cartographic map services In 2018 the COA provided public mapping services such as the latest aerial photographic maps, cadastral maps, and offline maps. We also developed a reference database and developed tools such as cadastral positioning, a program for mobile on- site surveys, and historical image accessing services. The COA also produced 6,421 high-resolution aerial orthophotographs, for use in national land planning. Moreover, we integrated the cartographic material of various organizations and constructed an agricultural “geographic information warehousing center,” which provides screening, selection, and export services. (b) Developing business application services To organize data from crop surveys and crop photography interpretation, the COA collected the results of remote sensing interpretation (about 10 million pieces of data) and details about cash relief for crops affected by natural disasters (about four million pieces of data) from over the years to prepare 32 maps (in 20 categories) of the distribution of major crops and disaster- prone crop areas, to be used in crop production management. We also organized data about the distribution of livestock farms nationwide, and by making comparisons using standardization of cadastral and address data, acquired spatial information on 29,823 livestock farms, thereby strengthening management of such farms. In order to integrate the COA’s on-site surveying work, we applied automated positioning, direction, and distance determination functions of photography to develop an “on-site survey APP.” We combined this with the crowdsourcing model and completed on-site surveys of second- crop-season crops on 80,068 hectares of land under the jurisdiction of the Yunlin Irrigation Association, producing 697,363 pieces of on-site crop image data, which we combined with expert knowledge and photo image recognition technology to develop a predictive model for production. The COA further constructed a Web map system entitled “the food administration spatial information system,” integrating cartographic data of various types 64