A low cost mobile mapping system (LCMMS) for field data acquisition: a potential use to validate aerial/satellite building damage assessment

Among the major natural disasters that occurred in 2010, the Haiti earthquake was a real turning point concerning the availability, dissemination and licensing of a huge quantity of geospatial data. In a few days several map products based on the analysis of remotely sensed data-sets were delivered to users. This demonstrated the need for reliable methods to validate the increasing variety of open source data and remote sensing-derived products for crisis management, with the aim to correctly spatially reference and interconnect these data with other global digital archives. As far as building damage assessment is concerned, the need for accurate field data to overcome the limitations of both vertical and oblique view satellite and aerial images was evident. To cope with the aforementioned need, a newly developed Low-Cost Mobile Mapping System (LCMMS) was deployed in Port-au-Prince (Haiti) and tested during a five-day survey in FebruaryMarch 2010. The system allows for acquisition of movies and single georeferenced frames by means of a transportable device easily installable (or adaptable) to every type of vehicle. It is composed of four webcams with a total field of view of about 180 degrees and one Global Positioning System (GPS) receiver, with the main aim to rapidly cover large areas for effective usage in emergency situations. The main technical features of the LCMMS, the operational use in the field (and related issues) and a potential approach to be adopted for the validation of satellite/aerial building damage assessments are thoroughly described in the articl

Identifying collapsed buildings

THE WORK TO RECOVER AND REBUILD FOLLOWING an earthquake requires reliable information on the condition of structures in the affected areas. In developed areas, efforts to gather this information can be time-consuming and prone to errors, often resulting in incomplete or inaccurate information. A new, software-based methodology to recognize collapsed buildings utilizes classification of satellite images combined with height variation information. The methodology was demonstrated in a full-scale, real-life scenario by a team led by Prof. Valerio Baiocchi of the University of Rome. According to Baiocchi, the team’s work was intended to demonstrate the methodology on actual data available for the entire city of L’Aquila in the Abruzzo region of central Italy, in an actual and complete simulation of quick damage assessment in a real emergency. The team utilized satellite imagery of the city of L’Aquila, which experienced a magnitude 6.3 earthquake on April 6, 2009. The work demonstrated a robust classification of collapsed structures that was completed quickly and with good confidence

The function of remote sensing in support of environmental policy

Limited awareness of environmental remote sensing’s potential ability to support environmental policy development constrains the technology’s utilization. This paper reviews the potential of earth observation from the perspective of environmental policy. A literature review of “remote sensing and policy” revealed that while the number of publications in this field increased almost twice as rapidly as that of remote sensing literature as a whole (15.3 versus 8.8% yr−1), there is apparently little academic interest in the societal contribution of environmental remote sensing. This is because none of the more than 300 peer reviewed papers described actual policy support. This paper describes and discusses the potential, actual support, and limitations of earth observation with respect to supporting the various stages of environmental policy development. Examples are given of the use of remote sensing in problem identification and policy formulation, policy implementation, and policy control and evaluation. While initially, remote sensing contributed primarily to the identification of environmental problems and policy implementation, more recently, interest expanded to applications in policy control and evaluation. The paper concludes that the potential of earth observation to control and evaluate, and thus assess the efficiency and effectiveness of policy, offers the possibility of strengthening governance

Structural Building Damage Detection with Deep Learning: Assessment of a State-of-the-Art CNN in Operational Conditions

Remotely sensed data can provide the basis for timely and efficient building damage maps that are of fundamental importance to support the response activities following disaster events. However, the generation of these maps continues to be mainly based on the manual extraction of relevant information in operational frameworks. Considering the identification of visible structural damages caused by earthquakes and explosions, several recent works have shown that Convolutional Neural Networks (CNN) outperform traditional methods. However, the limited availability of publicly available image datasets depicting structural disaster damages, and the wide variety of sensors and spatial resolution used for these acquisitions (from space, aerial and UAV platforms), have limited the clarity of how these networks can effectively serve First Responder needs and emergency mapping service requirements. In this paper, an advanced CNN for visible structural damage detection is tested to shed some light on what deep learning networks can currently deliver, and its adoption in realistic operational conditions after earthquakes and explosions is critically discussed. The heterogeneous and large datasets collected by the authors covering different locations, spatial resolutions and platforms were used to assess the network performances in terms of transfer learning with specific regard to geographical transferability of the trained network to imagery acquired in different locations. The computational time needed to deliver these maps is also assessed. Results show that quality metrics are influenced by the composition of training samples used in the network. To promote their wider use, three pre-trained networks—optimized for satellite, airborne and UAV image spatial resolutions and viewing angles—are made freely available to the scientific community

Collaborative damage mapping for emergency response : the role of Cognitive Systems Engineering

Remote sensing is increasingly used to assess disaster damage, traditionally by professional image analysts. A recent alternative is crowdsourcing by volunteers experienced in remote sensing, using internet-based mapping portals. We identify a range of problems in current approaches, including how volunteers can best be instructed for the task, ensuring that instructions are accurately understood and translate into valid results, or how the mapping scheme must be adapted for different map user needs. The volunteers, the mapping organizers, and the map users all perform complex cognitive tasks, yet little is known about the actual information needs of the users. We also identify problematic assumptions about the capabilities of the volunteers, principally related to the ability to perform the mapping, and to understand mapping instructions unambiguously. We propose that any robust scheme for collaborative damage mapping must rely on Cognitive Systems Engineering and its principal method, Cognitive Task Analysis (CTA), to understand the information and decision requirements of the map and image users, and how the volunteers can be optimally instructed and their mapping contributions merged into suitable map products. We recommend an iterative approach involving map users, remote sensing specialists, cognitive systems engineers and instructional designers, as well as experimental psychologists

Standard Operating Procedure - Collaborative Spatial Assessment CoSA - Release 1.0

The purpose of this Standard Operating Procedure (SOP) is to establish uniform procedures pertaining to the preparation for, the performance of, and the reporting of COllaborative (geo) Spatial Assessment (CoSA). CoSA provides a synoptic, unbiased assessment over the impact area of a disaster, which feeds the two main recovery perspectives of the Post-Disaster Needs Assessment (PDNA): i) the valuation of damages and losses carried out through the Damage and Loss Assessment (DaLA) methodology; and ii) the identification of human impacts and recovery needs carried out though the Human Recovery Needs Assessment (HRNA). CoSA is distinct from other geospatial and remote sensing based assessments because it i) draws on the collaborative efforts of distributed capacities in remote sensing and geospatial analysis, ii) aims to achieve the highest possible accuracy in line with the requirements of the PDNA and iii) tries to do so under stringent timing constraints set by the PDNA schedule. The current SOP will aid in ensuring credibility, consistency, transparency, accuracy and completeness of the CoSA. It is a living document, however, that will be enriched with new practical experiences and regularly updated to incorporate state-of-the-art procedures and new technical developments.JRC.DG.G.2-Global security and crisis managemen