Yapay Zeka, Kasırga Sonrası Afet Kurtarmasını Nasıl Dönüştürüyor

Yapay Zeka Doğal Afet Kurtarmasını Nasıl Geliştiriyor

When natural catastrophes strike, the immediate need is to save the local inhabitants and restore infrastructure to a functional state, especially power, fresh water, and healthcare.

Bu, iklim değişikliği ve artan nüfus nedeniyle son on yılda bu tür olayların maliyeti sürekli artan bir sorun haline geliyor.

Kaynak: Spire

Bununla birlikte, bir topluluğun gerçek iyileşmesi çok daha uzun sürer; hasarların değerlendirilmesi ve ardından yeniden inşa edilmesi aylar, hatta yıllar alabilir.

Often, this is delayed by the sheer overwhelming load of problems to identify and properly assess. Individual inspection of each building can take a lot of time, especially if insurance companies, emergency services, and other stakeholders are short on personnel.

“Manuel saha denetimleri iş gücü yoğun ve zaman alıcıdır, genellikle kritik müdahale çabalarını geciktirir.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

This could now change thanks to AI technology. Two researchers at Texas A&M University have developed an AI system using remote sensing, deep learning, and restoration models to predict accurately in less than one hour tornado damage assessments and recovery.

This method could help better organize recovery efforts, and was published in Sustainable Cities and Society¹, under the title “Post-tornado automated building damage evaluation and recovery prediction by integrating remote sensing, deep learning, and restoration models”.

“Our method uses high-resolution sensing imagery and deep learning algorithms to generate damage assessments within hours, immediately providing first responders and policymakers with actionable intelligence.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

Kasırga Yıkımı: ABD’de Artan Bir Tehdit

When unleashed, nature can be devastating for people and the cities they live in. One such example is tornadoes, a relatively regular occurrence in many parts of the world, including the so-called Tornado Alley in the USA.

Kaynak: Weather and Radar

Örneğin, 2011 baharında Missouri eyaletindeki Joplin, tahmini 200 mil/saat (321 km/s) üzerindeki rüzgarlarıyla bir EF5 kasırgası tarafından yıkıldı. Fırtına 161 kişiyi öldürdü, 1.000’den fazla kişiyi yaraladı ve yaklaşık 8.000 ev ve işletmeye zarar verip onları yok etti. Kasırga, şehrin yoğun nüfuslu güney-orta bölgesinde bir mil genişliğinde bir yol açarak kilometrelerce kırık enkaz bıraktı ve 2 milyar doların üzerinde bir zarara yol açtı.

Bu ay sadece, ölümcül kasırgalar birçok evi yok etti ve ABD’nin Orta Batısı ve Güneyinde büyük hasara neden oldu.

Bu kasırgaların neden olduğu hasarın tam ölçeğini kavramak muhtemelen çok zaman alacak. Ve burada Texas A&M Üniversitesi araştırmacılarının yardımcı olabileceğini düşünüyorlar.

Kasırga Sonrası Hasar Değerlendirmesindeki Zorluklar

After such an event, it is the standard procedure to have an array of first responders, city planners, and insurance experts move in to assess the destruction and how to react to it.

This is especially true with tornadoes, as the wind and heavy things lifted by them can destroy even the strongest buildings.

“Post-disaster damage surveys are typically time-consuming and labor-intensive, primarily focused on continuous model and code refinement rather than immediate and rapid updates.”

Some tentative steps have been made to use existing data to analyze the post-catastrophe damages, notably Geographic Information Systems (GIS), which bring together multiple layers of data about a given location.

But the way this data is used is often insufficient to provide accurate evaluations. It also requires a lot of manual interventions and human judgment to be turned into usable metrics. This is where adding extra information and AI can help.

Hasar Değerlendirmesi ve İyileşme için Yapay Zeka Destekli Model

Mevcut Kaynakları Yapay Zeka ile Birleştirme

The researchers combined 3 different tools together: remote sensing, deep learning, and restoration modeling.

To better evaluate the immediate damages and changes after the catastrophe, they used remote sensing like high-resolution satellite or aerial images.

“These images are crucial because they offer a macro-scale view of the affected area, allowing for rapid, large-scale damage detection.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

Then, deep learning methods were used to automatically analyze these images to identify the severity of the damage accurately.

The AI was trained on thousands of images of previous disasters and learned to recognize visible signs of damage such as collapsed roofs, missing walls, and scattered debris. It then classifies every building into categories like no damage, moderate damage, major damage, or destroyed.

The last element is restoration modeling. One part is using data about infrastructure details and community factors, like income levels or access to resources.

Another part is using existing and tested recovery models to judge how long it might take for homes and neighborhoods to recover under different funding or policy conditions.

This completely changes how damage assessment is performed:

• Uzaktan algılama, tüm durumun anlık bir özetini sunar.
• Yapay zeka modeli bu verileri bir saatten kısa sürede analiz edebilir; bu, sahada çalışan insanların hasar değerlendirmesini aylarca sürdürmesine kıyasla çok daha hızlıdır.
• İyileştirme modellemesi, yapay zeka değerlendirmesini hangi bölgelerin en çok yardıma ihtiyaç duyduğunu ve hangi kaynakların gerektiğini gösteren uygulanabilir metriklere dönüştürür.

“Ultimately, this research bridges the gap between rapid disaster assessment and strategic long-term recovery planning, offering a risk-informed yet practical framework for enhancing post-tornado resilience.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

Yapay Zeka Modelini Gerçek Dünya Kasırga Verileriyle Doğrulama

To validate this approach, the researchers looked back at the 2011 Joplin tornado catastrophe.

This event was extensively documented, creating a rich dataset that could be used as a backtest for the AI system. The calculated assessments could then be compared to real-life, on-the-ground damage assessments done at the time.

And the AI-generated results proved remarkably close to the historical data. It also gave a record of how the catastrophe unfolded.

“One of the most interesting findings was that, in addition to detecting damage with high accuracy, we could also estimate the tornado’s track.

By analyzing the damage data, we could reconstruct the tornado’s path, which closely matched the historical records, offering valuable information about the event itself.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

 Yapay Zeka Modelini Diğer Doğal Afetlere Ölçeklendirme

While it was developed and back-tested for tornado-caused damage, this method could be deployed for other situations, like hurricanes and earthquakes, as long as satellites can detect damage patterns.

This limitation could be less of a problem than initially expected, even if damages from earthquakes are, for example, less easily visible from the sky than blown-off roofs. This is because the model learns from real-life examples, and we often see AIs being able to detect patterns invisible to the human eye.

“The key to the model’s generalizability lies in training it to use past images from specific hazards, allowing it to learn the unique damage patterns associated with each event.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

At the very least, it seems that the model will match well for another common disaster in the USA: hurricanes.

“We have already tested the model on hurricane data, and the results have shown promising potential for adapting to other hazards.”

Abdullah Braik – Texas A&M’de inşaat mühendisliği doktora öğrencisi

This matches another application of AI in this field, with better hurricane prediction now becoming a reality, including with AIs like Graphcast,  Spire, and Climavision.

Another extension of this research could be to move beyond damage assessment. It could be used for creating real-time updates on recovery progress and tracking recovery over time.

This sort of automated, AI-driven feedback could then inform policy and optimize the rebuilding efforts.

Uydu Verileri ve Yapay Zekaya Yatırım

Spire

Spire, dünyanın en büyük çok amaçlı uydu takımyıldızını özel sektörde işleten bir uzay veri şirketidir.

The company <strongly focuses on weather data, and its satellites can capture images in multiple spectra, giving a more data-rich picture of a given location.

For example, its satellite images of moisture measurement are precise up to 100m and can be used by farmers, but also insurance companies, commodity traders, environmental monitoring agencies, construction companies, and civil engineers to better understand soil condition and upcoming agricultural yields.

Kaynak: Spire

The company offers to build for its client their own proprietary satellite constellation, with the LEMUR satellite platform.

Kaynak: Spire

The company is active in security as well, notably with its aviation offer for Automatic Dependent Surveillance-Broadcast (ADS-B), which uses GPS to determine airspeed, location, and other information about aircraft.

Meanwhile, Spire has been selected for a $237M contract with the US Space Force to “to design, build, integrate, and operate small satellite buses for next-generation space experiments”.

It was also active in the maritime industry, with its satellites used for vessel tracking, but this branch of the company was acquired by Kpler in Nisan 2025.

Regarding AI, Spire is collaborating with NVIDIA (NVDA ) to integrate into  NVIDIA‘s Earth–2 Cloud APIs all of Spire’s Radio Occultation (RO) data and proprietary data assimilation (DA)

“Aligning Spire’s proprietary data and unmatched global weather coverage with NVIDIA’s cutting-edge technology and expertise positions us to markedly elevate the accuracy of weather prediction. This collaboration will help ensure our customers are not just informed but empowered to proactively address the evolving climate landscape.”

Michael Eilts – Spire’da hava ve iklim genel müdürü

Overall, Spire is a data company embracing the conjunction of satellite imagery and AI for better weather forecasts, agriculture prediction, tracking of airplanes, and even defense purposes.

It is now reaching a scale where it could become profitable, an important potential turning point for investors, and maybe it will not need too much additional money raising after a successful $40M gross proceeds in selling new shares in Q1 2025.

Kaynak: Spire

En Son Spire (SPIR) Hisse Senedi Haberleri ve Gelişmeler

Çalışma Referans:

^{1. Abdullah M. Braikve Maria Koliou. Kasırga sonrası otomatik bina hasar değerlendirmesi ve iyileşme tahmini, uzaktan algılama, derin öğrenme ve iyileştirme modellerinin entegrasyonu. Sustainable Cities and Society. Cilt 123, 1 Nisan 2025, 106286. https://doi.org/10.1016/j.scs.2025.106286}