Welcome to the homepage of DRBoaST

Welcome to the homepage of DRBoaST


Disaster Resiliency through Big Open Data and Smart Things (DRBoaST) is a three-year applied research project funded by Academia Sinica Sustainability Science Research Program. It is a continuation of Project OpenISDM (Open Information Systems for Disaster Management), which ended in 2015. The main focus of OpenISDM was on a framework for building open and sustainable disaster management information systems. In contrast, the main focus of DRBoaST is on the generation and use of data. One of its objectives is to develop methods and tools for capturing, collection and generation of critically needed but missing data and information for disaster risk reduction. The other objective is to develop applications of available big and open data for disaster preparedness and responses, especially applications and services that can exploit open data and Internet of things to help us minimize personal dangers and reduce property damages and economic losses when disasters strike.

  DRBoaST project has five subprojects. They are

§ Strategies and Information for Disaster Resilient Communities (SIDiRC),

§ Real-Time Earthquake Information Cloud (RTEIC),

§ Crowdsourcing Situation Awareness Information (CSAI),

§ Disaster Scenario and Record Capture (DiSRC), and

§ Active Disaster Prepared Smart Living Environment (ADiPLE). 

Subprojects SiDiRC, RTEIC, and CSAI are extensions of OpenISDM efforts. The research directions of subprojects DiSRC and ADiPLE are new. They were motivated by critical needs and research and advanced development opportunities.

  Subproject SIDiRC will develop a virtual community-specific disaster information cloud consisting of databases for high disaster risk communities in Taiwan. The fine-grain GIS data and information in the database for each community are generated and kept up to date with the help of residents of the community. They supplement the GIS data in government DMIS to support community-specific disaster risk reduction decisions and operations.

  Subproject RTEIC aims to build a virtual real-time earthquake information cloud for disaster preparedness and response. On the one hand, the subproject will enhance TESIS (Taiwan Earthquake Science Information System) [1] built within the OpenISDM project with new capabilities made feasible by continuing advances in sensor and analysis technologies. On the other hand, the subproject will develop the underlying earth science and methods for using GIS information and observational data on earthquake-induced geo-hazards crowdsourced from trained volunteers immediately after significant earthquakes for many purposes. Examples include fine-scale assessments of damages and new disaster risks and predictions of earthquake-triggered compound disasters.  

  When crowdsourcing observational data, a disaster surveillance system must be able to make effective use of qualified volunteers, guide them in their exploration of the threatened area and process reports from them in real-time to extract decision support information of good and quantifiable quality. CROSS (CROwdsouring Support system for disaster Surveillance) [2-5] was designed and partially prototyped in OpenISDM project to meet these critical needs since modern platforms for crowdsourcing and mapping crisis information (e.g., Ushahidi) and participatory sensing lack the required capabilities. Subproject CSAI will enhance existing components of CROSS and integrate them into Ushahidi.

  Subproject DiSRC was motivated the fact that existing disaster historical records are almost solely for human consumption. Their effective usages are hampered and limited to a great extent: It is nearly impossible to extract from the records machine-readable data as input to modern analysis and simulation tools for purposes of assuring data completeness and consistency, assessing risk reduction strategies, tuning standard operating procedures, educating the public and so on. The subproject aims to develop disaster scenario capture, quality assurance and real-time quality control, and history record authoring technologies needed to produce machine-readable, high quality 3D and 4D historical data and information that can be processed by tools and can be easily translated into human readable form to provide input to authors of human-readable historical records.

  Today, disaster alerts and early warnings sent by authorities in most part of developed world are in the standard CAP (Common Alert Protocol[1]) format and machine readable. The alerts are processed by emergency alert systems/services, which in turn warn people via mobile phones and multimedia. A better alternative is to deliver CAP alerts directly to smart devices and mobile APPs and active emergent response systems (AERS) [6, 7] containing them and have them process the alerts and take actions in location- and environment-specific ways to keep us from harm and minimize property damages and economical loss when the disasters forewarned by the alerts strike. Subproject ADiPLE will develop the elements of technology and infrastructure needed to enable the pervasive use of AERS within smart homes, buildings and environments and exploit the use of 3D-4D data and information provided by BIM (Building Information Model) and facility management systems to support decisions of AERS of large, complex buildings. 

   Anticipated accomplishments and deliverables include a virtual community-specific disaster information cloud; a virtual real-time earthquake information cloud; platform, APPs and tools for crowdsourcing disaster surveillance data; active disaster response system for smart living environments; prototype components of disaster scenario record capture and authoring system; and technical and theoretical results that underpin these prototypes or enable us to bound the merits and limitations of our solutions. 

[1] CAP (Common Alert Protocol), v1.2, http://docs.oasis-open.org/emergency/cap/v1.2/CAP-v1.2-os.html