Structured and Unstructured Machine Learning for Crowdsourced Spatial Data
نویسندگان
چکیده
Recent years have seen a significant increase in the number of applications requiring accurate and up-to-date spatial data. In this context crowdsourced maps such as OpenStreetMap (OSM) have the potential to provide a free and timely representation of our world. However, one factor that negatively influences the proliferation of these maps is the uncertainty about their data quality. This paper presents structured and unstructured machine learning methods to automatically assess and improve the semantic quality of streets in the OSM database.
منابع مشابه
Georeferencing Semi-Structured Place-Based Web Resources Using Machine Learning
In recent years, the shared content on the web has had significant growth. A great part of these information are publicly available in the form of semi-strunctured data. Moreover, a significant amount of these information are related to place. Such types of information refer to a location on the earth, however, they do not contain any explicit coordinates. In this research, we tried to georefer...
متن کاملIntroduction to Machine Learning and Network Analytics in Finance Minitrack
We are experiencing enormous growth in the interest of application of various computational methods in finance, which is the consequence of various developments in the last 15 years. As a result, the number and importance of contributions utilizing various machine learning techniques and network analytics has increased significantly in many areas of finance. The presence of previously unprecede...
متن کاملThe machine learning process in applying spatial relations of residential plans based on samples and adjacency matrix
The current world is moving towards the development of hardware or software presence of artificial intelligence in all fields of human work, and architecture is no exception. Now this research seeks to present a theoretical and practical model of intuitive design intelligence that shows the problem of learning layout and spatial relationships to artificial intelligence algorithms; Therefore, th...
متن کاملPredicting Severe Sepsis Using Text from the Electronic Health Record
Employing a machine learning approach we predict, up to 24 hours prior, a diagnosis of severe sepsis. Strongly predictive models are possible that use only text reports from the Electronic Health Record (EHR), and omit structured numerical data. Unstructured text alone gives slightly better performance than structured data alone, and the combination further improves performance. We also discuss...
متن کاملA Reference-set Approach to Information Extraction from Unstructured, Ungrammatical Data Sources
This thesis investigates information extraction from unstructured, ungrammatical text on the Web such as classified ads, auction listings, and forum postings. Since the data is unstructured and ungrammatical, this information extraction precludes the use of rule-based methods that rely on consistent structures within the text or natural language processing techniques that rely on grammar. Inste...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016