Machine Learning on Satellite Radar Images to Estimate Damages After Natural Disasters

Xie, Boyi; Xu, Jeri; Jung, Jungkyo; Yun, Sang‐Ho; Zeng, Eric; Brooks, Edward M.; Dolk, Michaela; Narasimhalu, Lokeshkumar

doi:10.1145/3397536.3422349

Cited by 11 publications

(5 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, machine learning algorithms can be used to analyze data from remote sensing platforms to predict and monitor natural disasters, such as floods and landslides, which are common occurrences in Colombia (Arinta & Andi, 2019;Martínez-Álvarez & Morales-Esteban, 2019;Xie et al, 2020).…”

Section: Group Search Stringmentioning

confidence: 99%

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Pardo-Pardo,

Cuervo-Bejarano

2023

Agron. Colomb.

View full text Add to dashboard Cite

Agriculture 4.0 refers to innovations in technological tools used in agriculture to achieve different objectives, such as adapting the supply chain to avoid waste, increasing productivity and collecting mass data through ICT (Information and Communication Technologies) to meet the growing food demand of the population. The objective of this study is to conduct a systematic literature review to evaluate the impact of emerging technologies on sustainable fruit production. Initially, a bibliographic search was conducted on the technologies currently implemented in agriculture; the Bibliometrix library of the R Studio software was used, and then an analysis of relevant scientific publications published in the last ten years was carried out through the VOSviewer® software, which allowed the construction and visualization of bibliometric networks. The results show Europe and China as the leading regions in technological development, while developing countries face economic and research limitations; in Colombia, the use of Agriculture 4.0 is focused on the implementation of satellite images for monitoring agro-climatic conditions. In summary, Agriculture 4.0 aims to achieve economic, social, and environmental sustainability in the agri-food sector through data-generating technologies to improve production, reduce costs, and ensure food safety and quality. However, there is a technology gap between developed and developing countries that affects the adoption of these innovations. More support is therefore needed from governments, academia, and the private sector to drive innovation, training, and adoption of these technologies, which can contribute to the economic, social, and environmental development of the country.

show abstract

Section: Group Search Stringmentioning

confidence: 99%

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Pardo-Pardo,

Cuervo-Bejarano

2023

Agron. Colomb.

View full text Add to dashboard Cite

show abstract

“…Ronneberger et al [48] designed a skip-connected convolutional neural network that utilizes both contracting and expanding paths to enable cross-layer information transmission for biomedical image segmentation. Xie et al [9] proposed an image classification framework to classify the building damage status during a natural disaster from satellite radar images via ensemble models and deep learning networks. Zhu et al [10] developed a multimodal hypergraph learning approach that leverages vertices and hyperedges in hypergraphs to capture the complex similarities between different landmarks in content-based landmark image searching.…”

Section: Deep Learning-based Image Processing and Analyticsmentioning

confidence: 99%

CollabLearn: An Uncertainty-Aware Crowd-AI Collaboration System for Cultural Heritage Damage Assessment

Zhang

Zong

Kou

et al. 2022

IEEE Trans. Comput. Soc. Syst.

View full text Add to dashboard Cite

Cultural heritage sites are precious and fragile resources that hold significant historical, esthetic, and social values in our society. However, the increasing frequency and severity of natural and man-made disasters constantly strike the cultural heritage sites with significant damages. In this article, we focus on a cultural heritage damage assessment (CHDA) problem where the goal is to accurately locate the damaged area of a cultural heritage site using the imagery data posted on social media during a disaster event by exploring the collective strengths of both AI and human intelligence from crowdsourcing systems. Unlike other infrastructure-based solutions, social media platforms provide a more pervasive and scalable solution to acquire timely cultural heritage damage information during disaster events. Our work is motivated by the limitation of current AI solutions that fail to accurately model the complex cultural heritage damage due to the lack of essential human cultural knowledge to differentiate various damage types and identify the actual causes of the damage. Two critical technical challenges exist in solving our problem: 1) it is challenging to effectively detect the problematic cultural heritage damage estimation of AI in the absence of ground truth labels and 2) it is nontrivial to acquire accurate cultural background knowledge from the potentially unreliable crowd workers to effectively address the failure cases of AI. To address the above-mentioned challenges, we develop CollabLearn, an uncertainty-aware crowd-AI collaborative assessment system that explicitly explores the human intelligence from crowdsourcing systems to identify and fix AI failure cases and boost the damage assessment accuracy in CHDA applications. The evaluation results on real-world datasets show that CollabLearn consistently outperforms both the state-

show abstract

“…Li S et al [10] used FY-4A satellites and Doppler weather radar to observe the precipitation characteristics of sudden heavy rain events in the complex terrain of Southwest China. Xie B et al [11] use satellite radar images for machine learning to estimate losses after natural disasters. They will use satellite radar images and geographic data as input to classify the damage status of individual buildings after a major disaster event.…”

Section: Weather Forecast Based On Weather Radarmentioning

confidence: 99%

Survey on the Application of Deep Learning in Extreme Weather Prediction

et al. 2021

View full text Add to dashboard Cite

Because of the uncertainty of weather and the complexity of atmospheric movement, extreme weather has always been an important and difficult meteorological problem. Extreme weather events can be called high-impact weather, the ‘extreme’ here means that the probability of occurrence is very small. Deep learning can automatically learn and train from a large number of sample data to obtain excellent feature expression, which effectively improves the performance of various machine learning tasks and is widely used in computer vision, natural language processing, and other fields. Based on the introduction of deep learning, this article makes a preliminary summary of the existing extreme weather prediction methods. These include the ability to use recurrent neural networks to predict weather phenomena and convolutional neural networks to predict the weather. They can automatically extract image features of extreme weather phenomena and predict the possibility of extreme weather somewhere by using a deep learning framework.

show abstract

Machine Learning on Satellite Radar Images to Estimate Damages After Natural Disasters

Cited by 11 publications

References 5 publications

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

Assessing the impact of emerging technologies on sustainable fruit production: A systematic review of the literature

CollabLearn: An Uncertainty-Aware Crowd-AI Collaboration System for Cultural Heritage Damage Assessment

Survey on the Application of Deep Learning in Extreme Weather Prediction

Contact Info

Product

Resources

About