A Novel Methodology to Label Urban Remote Sensing Images Based on Location-Based Social Media Photos

Multisource and Multitemporal Data Fusion in Remote Sensing: A Comprehensive Review of the State of the Art

“…In the near future, all-day, all-weather and full spectrum acquisition segment datasets will be provided by commercial satellites, such as the Jilin-1 constellation, which has launched 10 fine-spatial resolution satellites by February 2018 and will have 60 satellites in orbit by 2020 with a capability of observing any global arbitrary point with a 30 minute revisit frequency [191]. Those video satellites with a (very) fine temporal and spatial resolution can effectively be exploited to monitor our location-based living environments like CCD cameras but on a much larger scale [192]. From a broader spatial perspective, new opportunities for humankind can be provided by the big remote sensing data acquired by those satellites jointly with social media data providing local and live/real time information to better monitor our living environment [192], especially in the applications of smart cities [193], [194], emergency and environmental hazards [195], [196], etc.. On the other hand, new challenges can appear from unprecedented access to a huge number of remote sensing data that are leading to a data-rich but knowledgepoor environment in a fast manner.…”

“…Those video satellites with a (very) fine temporal and spatial resolution can effectively be exploited to monitor our location-based living environments like CCD cameras but on a much larger scale [192]. From a broader spatial perspective, new opportunities for humankind can be provided by the big remote sensing data acquired by those satellites jointly with social media data providing local and live/real time information to better monitor our living environment [192], especially in the applications of smart cities [193], [194], emergency and environmental hazards [195], [196], etc.. On the other hand, new challenges can appear from unprecedented access to a huge number of remote sensing data that are leading to a data-rich but knowledgepoor environment in a fast manner. Here, the semantic gap of remote sensing data is usually caused due to the lack of certain land-cover or land-use remote sensing categories on site.…”

“…images before and after floods. In this context, it is possible to roughly determine the damaged area by unsupervised learning algorithms, but it is difficult to assess the details (e.g., the damage to transportation infrastructure) [192].…”

When the Power of the Crowd Meets the Intelligence of the Middleware

“…Crowdsensing aka Mobile Phone Sensing (MPS) is a promising approach to observe real-world phenomena at a very large scale. The many MPS applications that have emerged over the years illustrate well the added value: micro-blogging [25], mobile social networking [16], quantified selves [23], urban tomography [1], environmental monitoring [26], transportation [4], or dynamic indoor map construction [6] all benefit from MPS. It is our perspective that MPS has been and will continue generating drastic changes in the way we approach science in the years to come.…”

“…For illustration, we refer to our background experience with the Ambiciti/SoundCity solution (http://ambiciti.io/), which features a MPS application and cloud-based platform for monitoring the individual and collective exposure to environmental pollution, and particularly noise [8]. The development of Ambiciti started in 2014 to result in the first launch of the application with the support of the city of Paris in summer 2015 1 . We have then shown that the assimilation of MPS observations allows generating street-level noise pollution maps that enhance traditional simulated maps [20], provided the calibration of the application [21].…”