Humanitarian Remote Sensing

Remote Sensing

Remote sensing is acquiring information about objects or areas from a distance, typically from aircraft or satellites (Voigt et al., 2014). In the humanitarian sphere, this technology has been critical in improving response efforts by providing timely and accurate information about crises as they unfold (Bjorgo, 2004).

The evolution of remote sensing technology in the past decades has significantly impacted humanitarian response. Initially, the ability to obtain satellite imagery helped improve the management of humanitarian crises (Bjorgo, 2004). Early applications focused on flood detection and monitoring, such as the EU Joint Research Centre’s (EU JRC) flood analysis (De Groeve et al., 2007; De Groeve et al., 2006), which provided a rapid assessment of the extent of floods.

A lot of the groundbreaking work in humanitarian remote sensing was led by the United Nations Operational Satellite Applications Programme (UNOSAT). It delivers satellite imagery analysis for disaster response and humanitarian relief (Bjorgo, 2004). It was crucial in the 2011 Libya crisis, where mapping and damage assessments were vital in coordinating the response (Chan et al., 2012). The growing availability of web-based GIS technologies has provided humanitarian organizations with more interactive and accessible platforms for managing and interpreting remote sensing data (Kawasaki et al., 2013).  This integration has enhanced the ability to visualize and analyze multiple spatial data types, facilitating better decision-making during crises.

Crowdsourcing has been crucial in developing remote sensing applications in humanitarian contexts (Crowley & Chan, 2011). The advent of platforms such as OSM has democratized access to geospatial information (Meier, 2012, 2015). The Humanitarian OSM Team (HOT), a subsection of OSM, for instance, proved invaluable during the 2012 response to Hurricane Sandy, which hit New York City. Volunteers contributed to mapping efforts that supported disaster response (Huang et al., 2013). Initiatives like CrisisMappers, which utilize crowdsourced data to enhance situational awareness and response capabilities, have further supported this collaborative approach (Meier, 2015).

Advances in technology have also led to the increased combination of remote sensing with the crowdsourcing of citizen sensing and sensor web, thereby integrating local and hyper-local data collection through the collective efforts of citizens (Kamel Boulos et al., 2011). This approach was notably used in Somalia, leveraging the distributed power of citizen reporting to inform and improve response strategies (Western & Goldstein, 2011). Additionally, the concept of “humans as sensors” has been explored, where the collective intelligence of individuals contributes to real-time data collection. Combined with remote sensing, it enables more detailed and relevant analysis during emergencies (Avvenuti et al., 2016).

The Humanitarian Charter has facilitated rapid and free access to satellite imagery and geospatial data, enabling humanitarian organizations to utilize these resources more effectively (IFRC, 2013). For example, during the response to the earthquake in Haiti, satellite imagery provided crucial information for coordinating relief efforts (Dolinskaya et al., 2013). Ground truthing, the process of validating remote sensing data with on-ground observations, is critical to ensuring the accuracy and reliability of the information. Efforts to assess building data within OSM have demonstrated the importance of this validation process (Westrope et al., 2014).

However, the application of remote sensing in humanitarian response has its challenges. Data reliability, bias, and inclusivity arise, mainly when relying on crowdsourced data based on remote sensing (Smith, 2017). Ensuring the accuracy and relevance of the data collected through these means is crucial for effective response. These kinds of efforts highlight the need for robust filtering and verification mechanisms (Pavelin et al., 2018). The case studies from OneConcern and similar initiatives (Burks & Gupta, 2020; Lambert et al., 2021; McCarthy et al., 2020; Zhai et al., 2022) underscore the potential and perils of incorporating vast data streams into actionable intelligence. This is particularly important when using remote sensing in conflict zones since there are significant risks, as the security of crisis maps is paramount, and methodologies have been developed to ensure the safety and integrity of sensitive data (Chamales & Baker, 2011).

In their study of the use of satellite technologies in the humanitarian sector Caribou Space (2022) highlights the following insights and challenges when it comes to leveraging satellite technologies for humanitarian decision-making:

·     The business case for using remote sensing is not always clear.

·     There are unrealistic expectations of what can be done with satellite imagery.

·     There is caution over the application of new approaches and the adoption of new tools.

·     The tools available are too theoretical for use in real-life humanitarian contexts.

·     Humanitarians do not have equitable access to technical support.

·     The provision of data and training is not always fit for purpose.

·     There are often language barriers between technical and non-technical staff.

·     There are often ethical concerns around the use of imagery in specific contexts.

·     Satellite tools must be adapted to the fast pace and the complexity of crises.

·     Remote sensing is a crowded landscape, with, at times, a lack of a coherent approach.

Despite these challenges, remote sensing offers substantial benefits in humanitarian response. Integrating remote sensing data with GIS and other mapping technologies has enhanced the ability to visualize and analyze spatial information, supporting more effective decision-making (Zook et al., 2010). For example, during the response to Typhoon Haiyan that struck the Philippines in 2013, the coordination and information management capabilities provided by remote sensing and GIS were crucial in managing the disaster response (Van de Walle & Comes, 2014).

In recent years, there have also been rapid developments in satellite imagery technology. Planet Labs, a private company specializing in high-resolution LEO satellite imagery, has increasingly contributed to humanitarian response efforts by providing timely, detailed, high-resolution images of disaster-stricken areas. The company’s constellation of small satellites captures daily images of the entire Earth’s landmass, offering unparalleled access to up-to-date geospatial data (Bell, 2024). This capability is crucial in humanitarian contexts, where rapid damage assessment and situational awareness can save lives. During the 2017 hurricane season in the Caribbean, Planet Labs’ imagery was used to monitor the extent of the damage, guide relief efforts, and support recovery planning by providing precise and current visual information (Planet Team, 2017). Furthermore, the use of Planet Labs’ data in conflict zones has enabled humanitarian organizations to track displacement and infrastructure damage with high accuracy, facilitating more efficient allocation of resources and aiding in the protection of vulnerable populations (Bennett et al., 2022).

Bibliography:

Avvenuti, M., Cimino, M. G., Cresci, S., Marchetti, A., & Tesconi, M. (2016). A framework for detecting unfolding emergencies using humans as sensors. Springerplus, 5(1), 43. https://doi.org/10.1186/s40064-016-1674-y

Bell, C. (2024). Peace and Space. In C. Bell (Ed.), PeaceTech: Digital Transformation to End Wars (pp. 131-142). Springer International Publishing. https://doi.org/10.1007/978-3-031-38894-1_10

Bennett, M., Van Den Hoek, J., Zhao, B., & Prishchepov, A. (2022). Improving satellite monitoring of armed conflicts. Earth’s Future, 10(9), e2022EF002904. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022EF002904

Bjorgo, E. (2004). SATELLITE IMAGERY AND GIS FOR DISASTER RESPONSE & MANAGEMENT IN THE UNITED NATIONS. Proceedings ISCRAM2004,

Burks, L., & Gupta, A. (2020). PERFORMANCE METRICS TO EVALUATE PROBABILISTIC MODELS FOR STRUCTURAL DAMAGE DURING SEISMIC EVENTS. The 17th World Conference on Earthquake Engineering, Sendai, Japan.

Caribou Space. (2022). Satellite Technology for Humanitarian Decision Making. U. K. H. I. Hub. https://www.ukhih.org/focus-areas/accelerating-innovation-through-collaboration/satellite-technology-for-humanitarian-decision-making/

Chamales, G., & Baker, R. (2011). Securing crisis maps in conflict zones. 2011 IEEE Global Humanitarian Technology Conference,

Chan, J. L., Colombo, R., & Musani, A. (2012, 22/04/2012). Mapping libyan health facilities-A collaboration between crisis mappers and the world health organization. ISCRAM 2012 - 9th International Conference on Information Systems for Crisis Response and Management, Vancouver, Canada.

Crowley, J., & Chan, J. L. (2011). Disaster Relief 2.0: the Future of Information Sharing in Humanitarian Emergencies. OCHA. https://reliefweb.int/report/world/disaster-relief-20-future-information-sharing-humanitarian-emergencies

De Groeve, T., Kugler, Z., & Brakenridge, G. R. (2007, 23/10/2007). Near Real Time Flood Alerting for the Global Disaster Alert and Coordination System. Proceedings of ISCRAM 2007, Brussels, Belgium.

De Groeve, T., Vernaccini, L., Annunziato, A., Van de Walle, B., & Turoff, M. (2006, 12/10/2006). Global Disaster Alert and Coordination System. Proceedings of ISCRAM 2006, Newark, NJ, USA.

Dolinskaya, I., Smilowitz, K., & Chan, J. L. (2013). Integration of real-time mapping technology in disaster relief distribution.

Huang, A., Ma, A., Schmidt, S., Xu, N., Zhang, B., Meineke, L., Shi, Z., Chan, J. L., & Dolinskaya, I. (2013). Integration of real time data in urban search and rescue (Center for the Commercialization of Innovative Transportation Technology, Transportation Center, Northwestern University. Technical report, Issue. N. University. https://ccitt.northwestern.edu/documents/Integration_of_real_time_data_in_urban_search_and_rescue.pdf

IFRC. (2013). World disasters report 2013: Focus on technology and the future of humanitarian action (IFRC Disaster Report, Issue. IFRC. https://www.ifrc.org/document/world-disasters-report-2013-focus-technology-and-future-humanitarian-action

Kamel Boulos, M. N., Resch, B., Crowley, D. N., Breslin, J. G., Sohn, G., Burtner, R., Pike, W. A., Jezierski, E., & Chuang, K. Y. (2011). Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples. Int J Health Geogr, 10, 67. https://doi.org/10.1186/1476-072x-10-67

Kawasaki, A., Berman, M. L., & Guan, W. (2013). The growing role of web-based geospatial technology in disaster response and support. Disasters, 37(2), 201-221. https://doi.org/10.1111/j.1467-7717.2012.01302.x

Lambert, J. D., Rose, M., Ratcliff, J., O’Connor, M., Kedia, T., Oluic, S., Freeman, J., & Lovett, K. (2021). Ready for the Next Storm - AI-Enabled Situational Awareness for Disaster Response (National Security Report, Issue. J. Hopkins. https://www.jhuapl.edu/sites/default/files/2022-12/AIEnabledSAinDisasterResponse.pdf

McCarthy, N. F., Tohidi, A., Valero, M. M., Dennie, M., Aziz, Y., & Hu, N. (2020). A machine learning solution for operational remote sensing of active wildfires. IGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium,

Meier, P. (2012). Crisis mapping in action: How open source software and global volunteer networks are changing the world, one map at a time. Journal of map & geography libraries, 8(2), 89-100.

Meier, P. (2015). Digital humanitarians: how big data is changing the face of humanitarian response. Crc Press.

Pavelin, G., Bunja, D., & Mlinac, F. (2018). CRISIS MANAGEMENT AND HOW TO EMPOWER IT WITH CROWDSOURCING. In (pp. 166-173). Varazdin: Varazdin Development and Entrepreneurship Agency (VADEA).

Planet Team. (2017, 2017). Anatomy of a Catastrophe. Planet Labs. https://content.planet.com/c/anatomy-of-a-catastr?x=ujSTiy

Smith, C. (2017). A Case Study of Crowdsourcing Imagery Coding in Natural Disasters. In Data Analytics in Digital Humanities (pp. 217-230). Springer.

Van de Walle, B., & Comes, T. (2014). Risk accelerators in disasters: insights from the Typhoon Haiyan response on humanitarian information management and decision support CAiSE, Thessaloniki, Greece.

Voigt, S., Schoepfer, E., Fourie, C., & Mager, A. (2014). Towards semi-automated satellite mapping for humanitarian situational awareness. IEEE Global Humanitarian Technology Conference (GHTC 2014),

Western, J., & Goldstein, J. S. (2011). Humanitarian Intervention Comes of Age Lessons From Somalia to Libya. Foreign Affairs, 90(6), 48-+. ://WOS:000208711700005

Westrope, C., Banick, R., & Levine, M. (2014). Groundtruthing OpenStreetMap Building Damage Assessment. Procedia Engineering, 78, 29-39. https://doi.org/10.1016/j.proeng.2014.07.035

Zhai, C., Reilly, A. C., & Guikema, S. D. (2022). Individual learning as a driver of changes in community vulnerability under repeated hurricanes and changing climate. Risk Anal. https://doi.org/10.1111/risa.13955

Zook, M., Graham, M., Shelton, T., & Gorman, S. (2010). Volunteered geographic information and crowdsourcing disaster relief: a case study of the Haitian earthquake. World Medical & Health Policy, 2(2), 7-33. https://onlinelibrary.wiley.com/doi/10.2202/1948-4682.1069