With the rapid development of the internet and popularization of intelligent mobile devices, social media is evolving fast and contains rich spatial information, such as geolocated posts, tweets, photos, video, and audio. Those location-based social media data have offered new opportunities for hazards and disaster identification or tracking, recommendations for locations, friends or tags, pay-per-click advertising, etc. Meanwhile, a massive amount of remote sensing (RS) data can be easily acquired in both high temporal and spatial resolution with a multiple satellite system, if RS maps can be provided, to possibly enable the monitoring of our location-based living environments with some devices like charge-coupled device (CCD) cameras but on a much larger scale. To generate the classification maps, usually, labeled RS image pixels should be provided by RS experts to train a classification system. Traditionally, labeled samples are obtained according to ground surveys, image photo interpretation or a combination of the aforementioned strategies. All the strategies should be taken care of by domain experts, in a means which is costly, time consuming, and sometimes of a low quality due to reasons such as photo interpretation based on RS images only. These practices and constraints make it more challenging to classify land-cover RS images using big RS data. In this paper, a new methodology is proposed to classify urban RS images by exploiting the semantics of location-based social media photos (SMPs). To validate the effectiveness of this methodology, an automatic classification system is developed based on RS images as well as SMPs via big data analysis techniques including active learning, crowdsourcing, shallow machine learning, and deep learning. As the labels of RS training data are given by ordinary people with a crowdsourcing technique, the developed system is named Crowd4RS. The quantitative and qualitative experiments confirm the effectiveness of the proposed Crowd4RS system as well as the proposed methodology for automatically generating RS image maps in terms of classification results based on big RS data made up of multispectral RS images in a high spatial resolution and a large amount of photos from social media sites, such as Flickr and Panoramio.

Every day a large number of Earth observation (EO) spaceborne and airborne sensors from many different countries provide a massive amount of remotely sensed data. Those data are used for different applications, such as natural hazard monitoring, global climate change, urban planning, etc. The applications are data driven and mostly interdisciplinary. Based on this it can truly be stated that we are now living in the age of big remote sensing data. Furthermore, these data are becoming an economic asset and a new important resource in many applications. In this paper, we specifically analyze the challenges and opportunities that big data bring in the context of remote sensing applications. Our focus is to analyze what exactly does big data mean in remote sensing applications and how can big data provide added value in this context. Furthermore, this paper describes the most challenging issues in managing, processing, and efficient exploitation of big data for remote sensing problems. In order to illustrate the aforementioned aspects, two case studies discussing the use of big data in remote sensing are demonstrated. In the first test case, big data are used to automatically detect marine oil spills using a large archive of remote sensing data. In the second test case, content-based information retrieval is performed using high-performance computing (HPC) to extract information from a large database of remote sensing images, collected after the terrorist attack to the World Trade Center in New York City. Both cases are used to illustrate the significant challenges and opportunities brought by the use of big data in remote sensing applications.