基于ArcGIS的智能地质图综合

为解决常规地质图编制中存在的重复操作多、工作强度大、生产周期长、遗漏现象频发等缺点,本文立足地质编图业务需求,将地图综合、地质规律、地理信息系统、智能算法、数据库、云计算等多个交叉学科有机深度融合,创建了一种综合性的智能化地质编图方法与模式--智能地质图综合.地质图综合确定了图件编制的综合目标、处理对象、处理环节、综合方法和结果,可完成空间数据综合、属性数据综合、专家知识综合、模型综合等全方位的地质数据处理.地质图综合以"智绘地质"软件实现其功能,它以ArcGIS地质数据为处理对象,通过预处理、知识管理、数据综合、制图输出等多环节的一系列加工和处理,以专家有限参与的人机交互模式运行,智能化综合算法在专家知识驱动下,自动或半自动地处理地质体融合、地质界线生成等操作,可以高效地完成跨比例尺的地质图缩编、同比例尺或变比例尺的各种专题图件编制等工作.该方法在空白区编图、典型海岸带专题图开发等工作中进行了生产性验证,比传统编图方法节省约80%的编图工作量,成果图件数据符合地质规律、图形相似性好、图饰表达合理,且综合的多种图形效果人工难以达到.实际应用表明,智能地质图综合方法在工作效率、数据质量等方面明显优于传统地质编图方法.

Intelligent geological map generalization based on ArcGIS

Tedious repeated operations, tremendous work pressure, long production cycle and frequent omitted features are often found in traditional geological map compilation. For solving the above disadvantages, this paper stands on the basic compilation requirements of geological mapping, fuses some interdisciplinary techniques such as map generalization, geologic rules, GIS, intelligent algorithms, database and cloud computing together deeply, then establishes an novel integrated intelligent geologic map compilation method and pattern-Geological Map Generalization(GMG). GMG defines generalization goal, process object, process procedures, generalization method and result quality, etc. It involves all-round geologic data processing using spatial data generalization, property data generalization, expert knowledge generalization and data model generalization.GMG employs iMapower software to perform generalization tasks completely. Raw ArcGIS geologic data will be processed by a series of procedures such as data pre-processing, geoscientists’ knowledge management, geologic data generalization and cartographic export. Geoscientists’ knowledge drives intelligent generalization algorithms to complete geological body generalization, geological boundary generation operations semi-automatically or automatically under geoscientists interact with iMapower limitedly. Cross-scale geologic generalization, same scale and dynamic scale various thematic maps making can be fulfilled efficiently. Empty zone compilation and typical coastal thematic maps making as productive works play an important role on verifying validity of GMG. It just only uses about 20 percent of traditional generalization time. Achievement maps data meets geologic rules, geometry similarity and render styles, etc. Moreover GMG can give some wonderful generalization results that manual work is hard to realize. Practical applications show GMG method is obviously better than traditional compilation cover many aspects such as work efficiency, data quality control, etc.