Collaborative mapping | EPFL Graph Search

Collaborative mapping, also known as citizen mapping, is the aggregation of Web mapping and user-generated content, from a group of individuals or entities, and can take several distinct forms. With the growth of technology for storing and sharing maps, collaborative maps have become competitors to commercial services, in the case of OpenStreetMap, or components of them, as in Google Map Maker Waze and Yandex Map Editor. Volunteers collect geographic information and the citizens/individuals can be regarded as sensors within a geographical environment that create, assemble, and disseminate geographic data provided voluntarily by the individuals. Collaborative mapping is a special case of the larger phenomenon known as crowd sourcing, that allows citizens to be part of collaborative approach to accomplish a goal. The goals in collaborative mapping have a geographical aspect, e.g. having a more active role in urban planning. Especially when data, information, knowledge is distributed in a population and an aggregation of data is not available, then collaborative mapping can bring a benefit for the citizens or activities in a community with an e-Planing Platform. Extensions of critical and participatory approaches to geographic information systems combines software tools with a joint activities to accomplish a community goal. Additionally, the aggregated data can be used for a Location-based service like available public transport options at the geolocation where a mobile device is currently used (GPS-sensor). The relevance for the user at a specific geolocation cannot be represented with logic value in general (relevant=true/false). The relevance can be represented with Fuzzy-Logic or a Fuzzy architectural spatial analysis. Collaborative mapping applications vary depending on which feature the collaborative edition takes place: on the map itself (shared surface), or on overlays to the map. A very simple collaborative mapping application would just plot users' locations (social mapping or geosocial networking) or Wikipedia articles' locations (Placeopedia).

Land Cover Mapping From Multiple Complementary Experts Under Heavy Class Imbalance

Devis Tuia, Valérie Zermatten, Javiera Francisca Castillo Navarro, Xiaolong Lu

Deep learning has emerged as a promising avenue for automatic mapping, demonstrating high efficacy in land cover categorization through various semantic segmentation models. Nonetheless, the practical deployment of these models encounters important challen ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Technology Mapping Using Multi-output Library Cells

Giovanni De Micheli, Alessandro Tempia Calvino

Technology mapping transforms a technology-independent representation into a technology-dependent one given a library of cells. Even if technology libraries contain multi-output cells, state-of-the-art techniques fully exploit single-output cells only. Mul ...

2023