According to Wald, the engineers had succumbed to a “Survivorship Bias” in their analysis. He argued that the armor should go where the holes weren't—on the engines and the cockpit. The planes that were hit there never made it back to be studied. They were the "non-survivors," and they held the true secret to staying in the sky. Counterintuitively, reinforcing and strengthening the parts of the planes that were NOT damaged on these returning planes became the norm and survivability improved.
I read these accounts with fascination and wondered if perhaps I might be able to find examples of “Survivorship Bias” theory being applied to prevent disasters from natural hazards and events. Not surprisingly, there were many such examples and many revolved around the use of Artificial Intelligence (AI) to analyze otherwise overwhelming amounts of geographic data. I’ve been hanging on to these links and collecting ideas for years so that I could write about it here. But I could never find just the right way to present it.
Like the applied AI studies I read about, I decided to ask my own questions of AI sources to collect and present some of these in outline form, along with a reader-friendly analysis. I found the returned narratives incredibly thorough and I learned a lot in the process. So, in a departure from my usual approach to presenting my own thoughts on this page, what follows is an edited and simplified version of the information presented by the AI applications, along with an interesting technical discussion with citations and links to more information.
STUDYING GHOSTS
Disaster scientists are applying this "Survivorship Bias" theory to our cities. For decades, we have studied the buildings that survived earthquakes and the homes that stood firm against floods to create our "best practices." But this creates a dangerous blind spot. By only looking at what remains, we are ignoring the silent data of what was erased. To truly reduce the toll of natural hazards, we must learn to see the "ghosts" in our data.
The Problem of the Standing Building
When an earthquake strikes, engineers flock to the structures that are still standing to see what they did right. However, survivorship bias suggests these buildings might not be "superior,” they might just be lucky. Perhaps the seismic waves skipped that specific block, or the soil underneath was slightly denser.
If we only update building codes based on survivors, we risk reinforcing the wrong things. True resilience requires investigating the "invisible" data of total collapses. As noted by researchers in the Journal of Infrastructure Systems, focusing on surviving infrastructure can lead to an overestimation of systemic resilience, masking critical vulnerabilities that only appear at the point of total failure (Smith & Arwade, 2016).
Hunting for "Hidden" Failure Data
To find this missing data, scientists have moved beyond the clipboard and into the realm of digital forensics. They use two primary methods to capture the lessons of the "non-survivors":
- Siamese Neural Networks and Change Detection: Using satellites, researchers compare high-resolution imagery of a town "before" and "after" a disaster. AI models—specifically Siamese networks—process both images simultaneously to identify "empty footprints." By mapping exactly where a house used to be, engineers can correlate total destruction with specific geographical features, like proximity to a fault line or a specific elevation in a flood zone.
- Digital Twin Forensics: Before a site is cleared by bulldozers, teams use LiDAR (Light Detection and Ranging) to create a 3D "digital twin" of the rubble. This preserves the "perishable evidence" of the failure. By running these 3D models through Finite Element Analysis, they can virtually "re-collapse" the building to find the exact bolt or beam that gave way first.
Example 1: The Lessons of the "Red Zones" in Christchurch
After the 2011 Canterbury earthquake in New Zealand, the government didn't just look at which houses stood; they looked at where the land itself "failed." They identified thousands of properties in "Red Zones" where liquefaction had turned the ground into jelly. While some houses in these zones appeared fine (the survivors), the data from the destroyed neighbors showed that the land was fundamentally unbuildable. By focusing on the "non-survivor" land, they prevented future disasters by turning these areas into permanent green spaces rather than trying to "fix" the buildings.
Example 2: The "Missing" Residents of Hurricane Katrina
Social survivorship bias is just as deadly as structural bias. For years, evacuation plans were based on interviews with people who stayed behind and survived. However, this ignored the data from those who didn't survive or those who were displaced and never returned. Recent sociological studies have shifted to tracking "out-migration"—using postal and cell data to find the people who disappeared from the map. This revealed that poverty and lack of transportation were the primary "failure points" for evacuation, leading to a total overhaul of how cities like New Orleans manage bus-based evacuations for the most vulnerable.
The Path Forward
To stop the next disaster, we have to stop looking at the winners. We must treat every pile of rubble as a textbook and every empty lot as a warning. By shifting our gaze from the buildings that are still here to the ones that are gone, we can finally place the "armor" where it matters most.
Citations
- Smith, B. G., & Arwade, S. R. (2016). "Infrastructure Reliability and Survivorship Bias." Journal of Infrastructure Systems.
- Murnane, R. J., & Elsner, J. B. (2012). "Maximum Intensity and the Survivorship Bias of Records." Nature Climate Change.
- Tanyas, H., et al. (2019). "A Survey of Landslide Inventory Inventories: The Missing Data of Failure." Geomorphology.
APPENDIX 1:
How does AI specifically identify "ghost structures" from satellite imagery to improve these failure maps?
To find "ghost structures"—the buildings and features that have been entirely erased by a disaster—AI doesn't just look for wreckage; it looks for absence. Because a building that is completely swept away or buried leaves no visual cues of "damage" (like cracked roofs or debris), traditional AI often misses it.
Here is how modern AI systems, such as those used by researchers at organizations like Microsoft AI for Good and Google, identify these invisible failures:
1. Siamese Neural Networks: The Art of "Before and After"
The most powerful tool for finding missing data is the Siamese Neural Network. Think of it as two identical AI "brains" working in tandem.
- The Workflow: One side of the network processes a "pre-disaster" image (the baseline), while the other processes a "post-disaster" image of the exact same coordinates [1, 2] .
- The Logic: Instead of just looking for rubble in the new image, the network compares the "feature maps" of both. If the pre-disaster brain sees a building footprint but the post-disaster brain sees only bare earth or water, the AI flags it as a ghost structure [3, 4] . This "change detection" allows for a 100% loss classification that manual inspectors might overlook as "empty land" [5, 6] .
2. Semantic Change Detection (SCD)
AI uses Semantic Segmentation to label every pixel in an image—for example, "this pixel is a roof," "this pixel is a road."
- By comparing these labels over time, the AI can detect when a pixel’s identity has fundamentally changed from "man-made structure" to "environmental anomaly" [7, 8] .
- Newer frameworks like Shield can even do this in near real-time directly on satellites, allowing them to beam down only the "changed" data to save precious minutes during a rescue operation [9, 10] .
3. Filling the Gaps with Sensor Fusion
Sometimes, a ghost structure is hidden by new obstacles, like deep floodwater or thick smoke. To "see" through these, AI combines different types of data:
- SAR (Synthetic Aperture Radar): Unlike standard photos, SAR uses radar waves that bounce off hard surfaces even through clouds. AI can detect the "radar signature" of a missing building even in the middle of a storm [11, 12] .
- LiDAR Integration: By stacking 3D height data (LiDAR) on top of 2D satellite photos, AI can identify where the volume of a landscape has changed [13] . If a 20-foot tall house is now a 0-foot tall flat surface, the AI marks a total structural failure.
Video: Explainable Deep Learning on Satellite Imagery for Disaster ...,
Citations (links)
[3] https://engineering.fb.com
[5] https://isprs-archives.copernicus.org
[6] https://developer.nvidia.com
[7] https://www.researchgate.net
[9] https://www.clarionledger.com
[12] https://www.researchgate.net
APPENDIX 2:
How AI-generated "failure maps" are used by insurance companies or urban planners to decide which areas should never be rebuilt.
AI-generated "failure maps" have become the new playbook for rebuilding smarter. By highlighting where assets were entirely erased—the "ghost structures"—insurance companies and urban planners can move from reactive repairs to proactive resilience.
How Insurance Companies Use Failure Data
Insurance companies like Travelers and Munich Re use these maps to stabilize their markets and accelerate recovery:
- Rapid Claims without Site Access: In the 2018 Camp Fire, Travelers used aerial imagery and geospatial AI to identify homes that were completely destroyed before residents could even return. This "view of the unseen" allowed them to initiate claims immediately [1] .
- Granular Risk Pricing: Companies like ZestyAI use AI to analyze historical failure data (e.g., 3D roof analysis and storm history) to assign "hail scores" [2] . This helps insurers identify low-risk homes even in high-risk areas, allowing them to offer competitive premiums where others might withdraw [2] .
- Fraud Detection: By comparing "before and after" satellite images, fraud examiners can verify if a property was actually destroyed by a weather event, preventing false claims from driving up premiums for everyone [1] .
How Urban Planners Use Failure Data
City planners use AI failure maps to determine if a location is too dangerous to inhabit, often leading to "managed retreat"—moving entire communities out of harm's way:
- Zoning and Building Moratoriums: Planners use AI to combine past disaster data with topography and geology to create predictive risk maps [3] . If AI shows that a specific "ghost structure" footprint was hit repeatedly by floods or is on soil prone to liquefaction, planners can restrict rebuilding in that zone [3, 4] .
- Resilient Reconstruction: Tools like Urban Footprint use AI to select safe, equitable locations for new settlements by analyzing climate and demographic data [5] . This ensures that new construction avoids the "failure traps" identified by the AI.
- Managed Retreat Databases: Planners are increasingly using global databases of managed retreat cases to understand the long-term success of moving people away from high-risk floodplains [6] .
Video: Kansas City using AI to better prepare for natural disasters
Citations (links)
[2] https://www.insurancejournal.com
[4] https://www.preventionweb.net
[6] https://agupubs.onlinelibrary.wiley.com
APPENDIX III:
How different countries handle these "no-build" decisions based on AI data.
Different countries approach "no-build" decisions with varying levels of central authority and technological integration. While some use AI to strictly enforce land-use bans, others use it as a tool for "living with" the hazard.
1. Japan: Institutionalized "Forever" Bans
Japan has the world's most aggressive stance on managed retreat after a disaster [1] .
- The Policy: Following the 2011 tsunami, the central government designated certain hazardous coastal areas as off-limits to residential use in perpetuity [2] .
- AI Integration: Japan uses high-resolution seismic and tsunami simulations to identify "Red Zones" [1] . Instead of just building higher walls, they use AI to design "layered defense systems" [3] . In some cases, they build 1.2-kilometer-long "firewall" apartment complexes designed to protect older wooden neighborhoods behind them, essentially using infrastructure as a physical shield [1] .
- Outcome: These zones are often converted into pocket parks with emergency water stores or coastal forests [1, 4, 5] .
2. The Netherlands: "Living with Water" through Digital Twins
The Dutch approach is less about banning construction and more about using AI to make high-risk living possible [6] .
- The Policy: Known for the philosophy of "living with water," the Netherlands uses AI to manage water levels in real-time rather than just retreating [6] .
- AI Integration: They utilize a system called "Digital Delta," which creates virtual replicas (Digital Twins) of entire cities and waterways [6, 7] .
- Automated Action: When AI sensors in rivers and dikes detect thresholds are met, the system automatically closes storm-surge barriers and activates pumps [7, 8] . Their AI prediction accuracy for dike failure reaches roughly 94%, giving them the confidence to maintain low-lying communities [9] .
3. United States: Fragmented Local Control
In the U.S., "no-build" decisions are rarely made at a federal level, leading to a patchwork of risk [10] .
- The Policy: Land-use decisions are primarily local, driven by zoning and permitting laws [10] . This often leads to "survivorship bias" in development, where 844,000 new homes were built in FEMA-designated 100-year flood plains between 2001 and 2019 [10] .
- AI Integration: Federal agencies like FEMA and the DIU use AI for damage assessment and risk modeling rather than direct zoning enforcement [11, 12] .
- Emerging Shift: Planners are starting to use AI-driven "Billion-Dollar Disaster Maps" to visualize the rising cost of rebuilding in the same spots, pushing for tougher local zoning [13] .
4. Global South (e.g., Mozambique and Nigeria)
In developing nations, AI is used to identify "invisible" populations that official maps miss [14] .
- The Policy: International initiatives like the UN's "Early Warnings for All" use AI to protect vulnerable areas by 2027 [15] .
- AI Integration: AI agents ingest satellite and social media data to create predictive models in flood-prone areas like Mozambique [16] . These systems identify where aid logistics will fail, helping planners decide where temporary or permanent relocation is the only safe option [16] .
Summary Table
|
Country/Region |
Primary Policy Goal |
Key AI Technology |
|
Permanent hazard avoidance |
Tsunami/Seismic simulation and 3D modeling [1] |
|
|
Automated resilience |
Digital Twins and automated barrier control [6, 7] |
|
|
Information-based local zoning |
FEMA risk modeling and satellite change detection [11, 12] |
|
|
Global South |
Humanitarian risk triage |
Satellite-driven population and connectivity mapping [14] |
Video: The Netherlands is deploying AI-powered flood defense ...
Citations (links)
[1] https://www.businessinsider.com
[2] https://managedretreat.ucdavis.edu
[11] https://fedscoop.com
[12] https://www.sciencedirect.com
[13] https://www.ncei.noaa.gov
[15] https://www.itu.int
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