Locational Technologies in Post-disaster Infrastructure Space

Disentangling ◽  
2021 ◽  
pp. 41-60
Author(s):  
Mimi Sheller
Keyword(s):  
Everyday Life ◽  
Mobile Communication ◽  
Disaster Recovery ◽  
Digital Communication ◽  
Open Data ◽  
Humanitarian Aid ◽  
Recovery Processes ◽  
Post Disaster

Drawing on research carried out in Haiti from 2010 to 2013, this chapter considers how mobile communication infrastructures and locational technologies are enrolled into uneven global assemblages of power that may have more, or less, democratizing effects depending on how they are performed. The takeoff of digital humanitarianism using platforms such as OpenStreetMap (OSM) was built upon idealistic beliefs in the power of open data and locational media. However, the inclusivity of digital communication is fragile, and disconnection arises even as organizations and individuals attempt to facilitate connection. This analysis of locational technologies in post-earthquake Haiti considers how humanitarian aid and post-disaster recovery processes might be improved by first recognizing the uneven topologies of accessibility within communication infrastructures; and second by building on local appropriations of connectivity within everyday life to envision and enact patchwork connections across diverse communication platforms, as well as strategic disconnections.

Social repair and structural inequity: implications for disaster recovery practice

2015 ◽  
Vol 6 (4) ◽  
pp. 454-467 ◽  
Author(s):  
Omer Aijazi
Keyword(s):  
Social Relationships ◽  
Disaster Recovery ◽  
Design Methodology ◽  
Content Type ◽  
Vulnerability Reduction ◽  
Recovery Processes ◽  
Post Conflict ◽  
Future Risk ◽  
Structural Inequities ◽  
Post Disaster

Purpose – This paper introduces a model of social repair to the language of disaster recovery that potentially provides a new way of conceptualizing reconstruction and recovery processes by drawing attention to the dismantling of structural inequities that inhibit post-disaster recovery. Design/methodology/approach – The paper first engages with the current discourse of vulnerability reduction and resilience building as embedded within a distinct politics of post-disaster recovery. The concept of social repair is then explored as found within post-conflict and reconciliation literature. For application within the context of natural disasters, the concept of social repair is modified to have evaluative and effectiveness significance for disaster recovery. A short case example is presented from post-flood Pakistan to deepen our understanding of the potential application and usage of a social repair orientation to disaster recovery. Findings – The paper recommends that the evaluative goals of post-disaster recovery projects should be framed in the language of social repair. This means that social relationships (broadly defined) must be restored and transformed as a result of any disaster recovery intervention, and relationship mapping exercises should be conducted with affected communities prior to planning recovery interventions. Originality/value – Current discourses of disaster recovery are rooted within the conceptual framings of reducing vulnerabilities and building resilience. While both theoretical constructs have made important contributions to the disaster recovery enterprise, they have been unable to draw sufficient attention to pre-existing structural inequities. As disaster recovery and reconstruction projects influence the ways communities negotiate and manage future risk, it is important that interventions do not lead to worsened states of inequity.

scholarly journals A Conceptual Framework Integrating “Building Back Better” and Post-Earthquake Needs for Recovery and Reconstruction

2021 ◽  
Vol 13 (10) ◽  
pp. 5608
Author(s):  
Manjiang Shi ◽  
Qi Cao ◽  
Baisong Ran ◽  
Lanyan Wei
Keyword(s):  
Disaster Recovery ◽  
Social Process ◽  
Principal Component ◽  
Assessment Method ◽  
International Consensus ◽  
Long Term Stability ◽  
The People ◽  
Policy Guidance ◽  
The Impact ◽  
Post Disaster

Global disasters due to earthquakes have become more frequent and intense. Consequently, post-disaster recovery and reconstruction has become the new normal in the social process. Through post-disaster reconstruction, risks can be effectively reduced, resilience can be improved, and long-term stability can be achieved. However, there is a gap between the impact of post-earthquake reconstruction and the needs of the people in the disaster area. Based on the international consensus of “building back better” (BBB) and a post-disaster needs assessment method, this paper proposes a new (N-BBB) conceptual model to empirically analyze recovery after the Changning Ms 6.0 earthquake in Sichuan Province, China. The reliability of the model was verified through factor analysis. The main observations were as follows. People’s needs focus on short-term life and production recovery during post-earthquake recovery and reconstruction. Because of disparities in families, occupations, and communities, differences are observed in the reconstruction time sequence and communities. Through principal component analysis, we found that the N-BBB model constructed in this study could provide strong policy guidance in post-disaster recovery and reconstruction after the Changning Ms 6.0 earthquake, effectively coordinate the “top-down” and “bottom-up” models, and meet the diversified needs of such recovery and reconstruction.

scholarly journals Building Damage Detection Using U-Net with Attention Mechanism from Pre- and Post-Disaster Remote Sensing Datasets

2021 ◽  
Vol 13 (5) ◽  
pp. 905
Author(s):  
Chuyi Wu ◽  
Feng Zhang ◽  
Junshi Xia ◽  
Yichen Xu ◽  
Guoqing Li ◽  
...  
Keyword(s):  
Damage Assessment ◽  
Large Scale ◽  
Binary Classification ◽  
Open Data ◽  
Building Damage ◽  
Attention Mechanism ◽  
Large Scale Dataset ◽  
Data Program ◽  
The Impact ◽  
Post Disaster

The building damage status is vital to plan rescue and reconstruction after a disaster and is also hard to detect and judge its level. Most existing studies focus on binary classification, and the attention of the model is distracted. In this study, we proposed a Siamese neural network that can localize and classify damaged buildings at one time. The main parts of this network are a variety of attention U-Nets using different backbones. The attention mechanism enables the network to pay more attention to the effective features and channels, so as to reduce the impact of useless features. We train them using the xBD dataset, which is a large-scale dataset for the advancement of building damage assessment, and compare their result balanced F (F1) scores. The score demonstrates that the performance of SEresNeXt with an attention mechanism gives the best performance, with the F1 score reaching 0.787. To improve the accuracy, we fused the results and got the best overall F1 score of 0.792. To verify the transferability and robustness of the model, we selected the dataset on the Maxar Open Data Program of two recent disasters to investigate the performance. By visual comparison, the results show that our model is robust and transferable.

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