Usability dimensions in collaborative GIS

Collaborative GIS requires careful consideration of the Human-Computer Interaction (HCI) and Usability aspects, given the variety of users that are expected to use these systems, and the need to ensure that users will find the system effective, efficient, and enjoyable. The chapter explains the link between collaborative GIS and usability engineering/HCI studies. The integration of usability considerations into collaborative GIS is demonstrated in two case studies of Web-based GIS implementation. In the first, the process of digitising an area on Web-based GIS is improved to enhance the user's experience, and to allow interaction over narrowband Internet connections. In the second, server-side rendering of 3D scenes allows users who are not equipped with powerful computers to request sophisticated visualisation without the need to download complex software. The chapter concludes by emphasising the need to understand the users' context and conditions within any collaborative GIS project. © 2006, Idea Group Inc

Extreme citizen science: lessons learned from initiatives around the globe

The participation of communities living in high conservation value areas is increasingly valued in conservation science and practice, potentially producing multiple positive impacts on both biodiversity and local people. Here, we discuss important steps for implementing a successful extreme citizen science project, based on four case studies from conservation projects with Pantaneiro fishers living in Brazilian Pantanal wetland, Baka hunter-gatherers and Fang farmers in lowland wet forest in Cameroon, Maasai pastoralists in Kenya, and Ju|'hoansi rangers living in the semiarid deserts of Namibia. We highlight the need for a high level of trust between the target communities and project developers, communities' right to choose the data they will be collecting, and researchers' openness to include new tools that were not initially planned. By following these steps, conservation scientists can effectively create bottom-up collaborations with those living on the frontlines of conservation through community-led extreme citizen science

Extreme citizen science: lessons learned from initiatives around the globe

The participation of communities living in high conservation value areas is increasingly valued in conservation science and practice, potentially producing multiple positive impacts on both biodiversity and local people. Here, we discuss important steps for implementing a successful extreme citizen science project, based on four case studies from conservation projects with Pantaneiro fishers living in Brazilian Pantanal wetland, Baka hunter-gatherers and Fang farmers in lowland wet forest in Cameroon, Maasai pastoralists in Kenya, and Ju|'hoansi rangers living in the semiarid deserts of Namibia. We highlight the need for a high level of trust between the target communities and project developers, communities' right to choose the data they will be collecting, and researchers' openness to include new tools that were not initially planned. By following these steps, conservation scientists can effectively create bottom-up collaborations with those living on the frontlines of conservation through community-led extreme citizen science

Multimodal image alignment through a multiscale chain of neural networks with application to remote sensing

International audienceWe tackle here the problem of multimodal image non-rigid registration, which is of prime importance in remote sensing and medical imaging. The difficulties encountered by classical registration approaches include feature design and slow optimization by gradient descent. By analyzing these methods, we note the significance of the notion of scale. We design easy-to-train, fully-convolutional neural networks able to learn scale-specific features. Once chained appropriately, they perform global registration in linear time, getting rid of gradient descent schemes by predicting directly the deformation. We show their performance in terms of quality and speed through various tasks of remote sensing multimodal image alignment. In particular, we are able to register correctly cadastral maps of buildings as well as road polylines onto RGB images, and outperform current keypoint matching methods