# Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning
## 1 Introduction  
Disasters are a persistent threat to human well-being, and disaster resilience is of paramount importance to the public. To achieve disaster resilience, technical and organizational resources play vital roles in securing adequate preparation, adaptable response, and rapid recovery [1]. During the phases of response and recovery, while disaster scenes are possibly still being unfolded, information collection via disaster reconnaissance is crucial for restoring the functionalities of communities and infrastructure systems. Disaster scenes, however, are perishable as a disaster recedes and recovery efforts progress. Traditional remote sensing (RS) technologies via orbital or airborne imaging sensors have long been recognized as a helpful tool that provides geospatial analytics for improving the efficacy of disaster reconnaissance. In general, RS technologies can provide data that record the disaster scenes, including unfolded hazards (e.g., flood inundation) and disastrous consequences (e.g., building damage). However, traditional RS platforms suffer from long time latency due to revisit periods, orbital maneuver, and the ensuing data processing [2]. Moreover, due to their orbital or airborne view, it is often impossible for traditional RS sensors to capture the elevation view of built objects (e.g., buildings) and other detailed structural conditions. Therefore, although it is less efficient in terms of spatial coverage, ground-based disaster reconnaissance is not replaceable by traditional remote sensing. In recent years, two emerging RS technologies are changing the normal of disaster reconnaissance. First, due to the ubiquity of digital cameras, smartphones, body-mounted recording devices, and social networking smart apps, which may be collectively termed smart devices, can seamlessly capture, process, and share images. This nearly real-timeimaging can record personal and public events, including disasters. Many researchers in Civil Engineering hence recognize the power of mobile devices for aiding disaster response, infrastructure inspection, and construction monitoring [3; 4; 5; 6]. The second is the use of small unmanned aerial vehicles (UAVs). As witnessed in recent years, since UAVs can provide high-resolution coverage of ground scenes at low above-ground-level (AGL) heights, many research efforts investigate the application of UAV-based RS [7; 8; 9; 10]. UAV imaging can be deployed with diverse modes, including survey-grade imaging similar to traditional airborne remote sensing. Nonetheless, recent trends include using a micro-sized UAV as a flying camera at a very low AGL, which is then similar to the imaging with the use of mobile devices, for example, via the so-called 'follow-me' drone [11]. In this paper, the authors propound that this non-traditional RS practice can conduce to the demand for real-time analytics arising from the post-disaster response and recovery activities. By recognizing their primary characteristic of using small and low-cost devices carried or operated by human users or operators, the term _mobile remote sensing_ is coined in this effort. It is noted that in this paper, the emphasis is on smart device-based imaging and the disaster-scene images from this venue. In the meantime, this choice is supported by the abundance of such images, as researchers have archived a large volume of mobile imagery data from numerous disaster reconnaissance activities (e.g., as found in DesignSafe-CI, a collaborative cloud-based workspace) [12]. Mobile RS provides untapped opportunities for extracting relevant information and understanding natural disasters. However, significant differences exist when comparing the processing methods for mobile RS images against traditional ones. First, traditional RS images are generally ortho-rectified and geo-referenced when provided for processing and understanding.