scholarly journals Deep Learning-Enabled Semantic Inference of Individual Building Damage Magnitude from Satellite Images

Algorithms ◽  
2020 ◽  
Vol 13 (8) ◽  
pp. 195 ◽  
Author(s):  
Bradley J. Wheeler ◽  
Hassan A. Karimi

Natural disasters are phenomena that can occur in any part of the world. They can cause massive amounts of destruction and leave entire cities in great need of assistance. The ability to quickly and accurately deliver aid to impacted areas is crucial toward not only saving time and money, but, most importantly, lives. We present a deep learning-based computer vision model to semantically infer the magnitude of damage to individual buildings after natural disasters using pre- and post-disaster satellite images. This model helps alleviate a major bottleneck in disaster management decision support by automating the analysis of the magnitude of damage to buildings post-disaster. In this paper, we will show our methods and results for how we were able to obtain a better performance than existing models, especially in moderate to significant magnitudes of damage, along with ablation studies to show our methods and results for the importance and impact of different training parameters in deep learning for satellite imagery. We were able to obtain an overall F1 score of 0.868 with our methods.

2018 ◽  
Vol 30 (2) ◽  
pp. 192
Author(s):  
Erda Rindrasih

Tourism has emerged as one of the largest and most rapidly growing economic sectors in the world. Nevertheless, many tourist destinations have been periodically confronted by natural disasters that threaten their survival as an industry by negatively impacting their image and safety perception. This research assessed tourists’ perception of the risk and images of a destination that is considered prone to natural disasters, by surveying 537 tourists in Yogyakarta and Bali. This study contributes to the debate on tourism development issues related to negative perceptions and images that have discouraged prospective tourists from visiting affected destinations. The results of the survey indicated that the occurrence of past disasters did not strongly influence tourists’ decision to visit Indonesia. Instead, the creation of the destination image was informed more by its current situation, and it is these current factors that may encourage or discourage potential tourists. These findings should signal to tourism planners that while environmental disasters are unavoidable, post-disaster rehabilitation of a destination’s image would significantly increase its chances of rebounding quickly.


Author(s):  
Shiv Kumar ◽  
Agrima Yadav ◽  
Deepak Kumar Sharma

The exponential growth in the world population has led to an ever-increasing demand for food supplies. This has led to the realization that conventional and traditional methods alone might not be able to keep up with this demand. Smart agriculture is being regarded as one of the few realistic ways that, together with the traditional methods, can be used to close the gap between the demand and supply. Smart agriculture integrates the use of different technologies to better monitor, operate, and analyze different activities involved in different phases of the agricultural life cycle. Smart agriculture happens to be one of the many disciplines where deep learning and computer vision are being realized to be of major impact. This chapter gives a detailed explanation of different deep learning methods and tries to provide a basic understanding as to how these techniques are impacting different applications in smart agriculture.


2020 ◽  
Vol 12 (10) ◽  
pp. 1581 ◽  
Author(s):  
Daniel Perez ◽  
Kazi Islam ◽  
Victoria Hill ◽  
Richard Zimmerman ◽  
Blake Schaeffer ◽  
...  

Coastal ecosystems are critically affected by seagrass, both economically and ecologically. However, reliable seagrass distribution information is lacking in nearly all parts of the world because of the excessive costs associated with its assessment. In this paper, we develop two deep learning models for automatic seagrass distribution quantification based on 8-band satellite imagery. Specifically, we implemented a deep capsule network (DCN) and a deep convolutional neural network (CNN) to assess seagrass distribution through regression. The DCN model first determines whether seagrass is presented in the image through classification. Second, if seagrass is presented in the image, it quantifies the seagrass through regression. During training, the regression and classification modules are jointly optimized to achieve end-to-end learning. The CNN model is strictly trained for regression in seagrass and non-seagrass patches. In addition, we propose a transfer learning approach to transfer knowledge in the trained deep models at one location to perform seagrass quantification at a different location. We evaluate the proposed methods in three WorldView-2 satellite images taken from the coastal area in Florida. Experimental results show that the proposed deep DCN and CNN models performed similarly and achieved much better results than a linear regression model and a support vector machine. We also demonstrate that using transfer learning techniques for the quantification of seagrass significantly improved the results as compared to directly applying the deep models to new locations.


2018 ◽  
Vol 30 (2) ◽  
pp. 192
Author(s):  
Erda Rindrasih

Tourism has emerged as one of the largest and most rapidly growing economic sectors in the world. Nevertheless, many tourist destinations have been periodically confronted by natural disasters that threaten their survival as an industry by negatively impacting their image and safety perception. This research assessed tourists’ perception of the risk and images of a destination that is considered prone to natural disasters, by surveying 537 tourists in Yogyakarta and Bali. This study contributes to the debate on tourism development issues related to negative perceptions and images that have discouraged prospective tourists from visiting affected destinations. The results of the survey indicated that the occurrence of past disasters did not strongly influence tourists’ decision to visit Indonesia. Instead, the creation of the destination image was informed more by its current situation, and it is these current factors that may encourage or discourage potential tourists. These findings should signal to tourism planners that while environmental disasters are unavoidable, post-disaster rehabilitation of a destination’s image would significantly increase its chances of rebounding quickly.


Author(s):  
Aparna U ◽  
Athira B ◽  
Anuja M V ◽  
Aswathy Ramakrishnan ◽  
Divya R

Collapse of man-made structures, such as buildings and bridges earth quakes and fire accident, occur with varying frequency across the world. In such a scenario, the survived human beings are likely to get trapped in the cavities created by collapsed building material. During post disaster rescue operations, searchand-rescue crews have a very limited or no knowledge of the presence, location, and number of the trapped victims. Deep learning is a fast-growing domain of machine learning, mainly for solving problems in computer vision. One of the implementation of deep learning is detection of objects including humans, based on video stream. Thus, the presence of a human buried under earthquake rubble or hidden behind barriers can be identified using deep learning. This is done with the help of USB camera which can be inserted into the rubble. Spotter also gives an audio message about the location of the human presence and gives the area where the human is likely to be present. Human detection is done with the help of Computer Vision using OpenCV.


Author(s):  
Anuraag Velamati Et.al

The world is quickly and continuously advancing towards better technological advancements that will make life quite easier for us, human beings [22]. Humans are looking for more interactive and advanced ways to improve their learning. One such dream is making a machine think like a computer, which lead to innovations like AI and deep learning [25]. The world is running at a higher pace in the domain of AI, deep learning, robotics and machine learning Using this knowledge and technology, we could develop anything right now [36]. As a part of sub-domain, the introduction of Convolution Neural Networks made deep learning extensively strong in the domain of image classification and detection [1]..The research that we have conducted is one of its kind. Our research used Convolution Neural Network, TensorFlow and Keras.


2021 ◽  
Vol 331 ◽  
pp. 04013
Author(s):  
Khairil Anwar

This study is about Minangkabau’s local wisdom in disaster mitigation. Minangkabau is an ethnic and cultural group that is still alive and developing today. This ethnic group is centered in West Sumatra in the highlands of the Bukit Barisan which stretches along the island of Sumatra and develops through migrating to various regions in the world. This ethnicity is the oldest tribe on earth which is characterized by the use of the hereditary system according to the maternal or matrilineal line. His leadership made the Minangkabau ethnic have various kinds of local wisdom, especially those directly related to disaster mitigation. The Minangkabau cultural center is located in an area that has a high intensity of natural disasters by its natural topography. In responding to their natural environment, the Minangkabau community has local wisdom in the form of a superstructure that regulates infrastructure and social structure in disaster mitigation. This local wisdom is found in various literacy and traditions of the Minangkabau community. This qualitative discussion uses the perspective of cultural materialism theory. It was found that the local wisdom of disaster mitigation includes human norms and attitudes towards nature; norms before a disaster occurs; and post-disaster policies. To anticipate disasters, there are rules regarding the processing and utilization of nature, such as the use of land, hills, deserts, and swamps. In the event of a disaster, there are rules such as building rangkiang and filling it with food reserves, doing the ijok tradition, and batangeh.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 46-47
Author(s):  
Ran An ◽  
Yuncheng Man ◽  
Shamreen Iram ◽  
Erdem Kucukal ◽  
Muhammad Noman Hasan ◽  
...  

Introduction: Anemia affects a third of the world's population with the heaviest burden borne by women and children. Anemia leads to preventable impaired development in children, as well as high morbidity and early mortality among sufferers. Inherited hemoglobin (Hb) disorders, such as sickle cell disease (SCD), are associated with chronic hemolytic anemia causing high morbidity and mortality. Anemia and SCD are inherently associated and are both prevalent in the same regions of the world including sub-Saharan Africa, India, and south-east Asia. Anemia and SCD-related complications can be mitigated by screening, early diagnosis followed by timely intervention. Anemia treatment depends on the accurate characterization of the cause, such as inherited Hb disorders. Meanwhile, Hb disorders or SCD treatments, such as hydroxyurea therapy, requires close monitoring of blood Hb level and the patient's anemia status over time. As a result, it is crucially important to perform integrated detection and monitoring of blood Hb level, anemia status, and Hb variants, especially in areas where anemia and inherited Hb disorders are the most prevalent. Blood Hb level (in g/dL) is used as the main indicator of anemia, while the presence of Hb variants (e.g., sickle Hb or HbS) in blood is the primary indicator of an inherited disorder. The current clinical standards for anemia testing and Hb variant identification are complete blood count (CBC) and High-Performance Liquid Chromatography (HPLC), respectively. State-of-the-art laboratory infrastructure and trained personnel are required for these laboratory tests. However, these resources are typically scarce in low- and middle-income countries, where anemia and Hb disorders are the most prevalent. As a result, there is a dire need for high accuracy portable point-of-care (POC) devices to perform integrated anemia and Hb variant tests with affordable cost and high throughput. Methods: In 2019, the World Health Organization (WHO) listed Hb electrophoresis as an essential in vitro diagnostic (IVD) technology for diagnosing SCD and sickle cell trait. We have leveraged the common Hb electrophoresis method and developed a POC microchip electrophoresis test, Hemoglobin Variant/Anemia (HbVA). This technology is being commercialized under the product name "Gazelle" by Hemex Health Inc. for Hb variant identification with integrated anemia detection (Fig. 1A&B). We hypothesized that computer vision and deep learning will enhance the accuracy and reproducibility of blood Hb level prediction and anemia detection in cellulose acetate based Hb electrophoresis, which is a clinical standard test for Hb variant screening and diagnosis worldwide (Fig. 1C). To test this hypothesis, we integrated, for the first time, a new, computer vision and artificial neural network (ANN) based deep learning imaging and data analysis algorithm, to Hb electrophoresis. Here, we show the feasibility of this new, computer vision and deep learning enabled diagnostic approach via testing of 46 subjects, including individuals with anemia and homozygous (HbSS) or heterozygous (HbSC or Sβ-thalassemia) SCD. Results and Discussion: HbVA computer vision tracked the electrophoresis process real-time and the deep learning neural network algorithm determined Hb levels which demonstrated significant correlation with a Pearson Correlation Coefficient of 0.95 compared to the results of reference standard CBC (Fig.1D). Furthermore, HbVA demonstrated high reproducibly with a mean absolute error of 0.55 g/dL and a bias of -0.10 g/dL (95% limits of agreement: 1.5 g/dL) according to Bland-Altman analysis (Fig. 1E). Anemia determination was achieved with 100% sensitivity and 92.3% specificity with a receiver operating characteristic area under the curve (AUC) of 0.99 (Fig. 1F). Within the same test, subjects with SCD were identified with 100% sensitivity and specificity (Fig. 1G). Overall, the results suggested that computer vision and deep learning methods can be used to extract new information from Hb electrophoresis, enabling, for the first time, reproducible, accurate, and integrated blood Hb level prediction, anemia detection, and Hb variant identification in a single affordable test at the POC. Disclosures An: Hemex Health, Inc.: Patents & Royalties. Hasan:Hemex Health, Inc.: Patents & Royalties. Ahuja:Genentech: Consultancy; Sanofi-Genzyme: Consultancy; XaTec Inc.: Consultancy; XaTec Inc.: Research Funding; XaTec Inc.: Divested equity in a private or publicly-traded company in the past 24 months; Genentech: Honoraria; Sanofi-Genzyme: Honoraria. Little:GBT: Research Funding; Bluebird Bio: Research Funding; BioChip Labs: Patents & Royalties: SCD Biochip (patent, no royalties); Hemex Health, Inc.: Patents & Royalties: Microfluidic electropheresis (patent, no royalties); NHLBI: Research Funding; GBT: Membership on an entity's Board of Directors or advisory committees. Gurkan:Hemex Health, Inc.: Consultancy, Current Employment, Patents & Royalties, Research Funding; BioChip Labs: Patents & Royalties; Xatek Inc.: Patents & Royalties; Dx Now Inc.: Patents & Royalties.


2019 ◽  
Vol 11 (20) ◽  
pp. 2427 ◽  
Author(s):  
Saman Ghaffarian ◽  
Norman Kerle ◽  
Edoardo Pasolli ◽  
Jamal Jokar Arsanjani

First responders and recovery planners need accurate and quickly derived information about the status of buildings as well as newly built ones to both help victims and to make decisions for reconstruction processes after a disaster. Deep learning and, in particular, convolutional neural network (CNN)-based approaches have recently become state-of-the-art methods to extract information from remote sensing images, in particular for image-based structural damage assessment. However, they are predominantly based on manually extracted training samples. In the present study, we use pre-disaster OpenStreetMap building data to automatically generate training samples to train the proposed deep learning approach after the co-registration of the map and the satellite images. The proposed deep learning framework is based on the U-net design with residual connections, which has been shown to be an effective method to increase the efficiency of CNN-based models. The ResUnet is followed by a Conditional Random Field (CRF) implementation to further refine the results. Experimental analysis was carried out on selected very high resolution (VHR) satellite images representing various scenarios after the 2013 Super Typhoon Haiyan in both the damage and the recovery phases in Tacloban, the Philippines. The results show the robustness of the proposed ResUnet-CRF framework in updating the building map after a disaster for both damage and recovery situations by producing an overall F1-score of 84.2%.


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