Satellite Imagery Analysis for Operational Damage Assessment in Emergency Situations

Segmentation of tumors in brain MRI images is a challenging task, where most recent methods demand large volumes of data with pixel-level annotations, which are generally costly to obtain. In contrast, image-level annotations, where only the presence of lesion is marked, are generally cheap, generated in far larger volumes compared to pixel-level labels, and contain less labeling noise. In the context of brain tumor segmentation, both pixel-level and image-level annotations are commonly available; thus, a natural question arises whether a segmentation procedure could take advantage of both. In the present work we: 1) propose a learning-based framework that allows simultaneous usage of both pixel-and image-level annotations in MRI images to learn a segmentation model for brain tumor; 2) study the influence of comparative amounts of pixel-and image-level annotations on the quality of brain tumor segmentation; 3) compare our approach to the traditional fully-supervised approach and show that the performance of our method in terms of segmentation quality may be competitive.

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“…A challenging task would be to test the developed capabilities on other types of image data, e.g. from remote sensing applications [25], [26].…”

Section: Discussionmentioning

confidence: 99%

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

Павлов

Artemov

Sharaev

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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“…Datasets for change detection are commonly structured in pairs of registered images of the same territory, made in distinct mo-ments in time, accompanied by image masks per each of the annotated changes. With the primary application being emergency mapping, most datasets typically feature binary masks annotating damaged structures across the mapped areas [27,48,12,8]. L'Aquila 2009 earthquake dataset [8] contains data spanning 1.5 × 1.5 km 2 annotated with masks of damaged buildings during the 2009 earthquake.…”

Section: Image Datasets For Change Detectionmentioning

confidence: 99%

“…Following [48], we use a pair of Ventura train images (4573 × 4418 px) for training or fine-tuning our models. As our goal is to study the effect of decreasing volumes of real-world data, we crop a random patch from these images, setting the ratio of patch area to the full image area to be 1, 1/2, 1/4, 1/8, and 1/16.…”

Section: The Evaluation Setupmentioning

confidence: 99%

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Kolos

Marin

Artemov

et al. 2019

Lecture Notes in Computer Science

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Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In this work, we propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. We provide a description of the pipeline for procedural geometry generation and rendering as well as an evaluation of the efficiency of produced datasets in a change detection scenario. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.

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“…Skoltech CDISE Petaflops supercomputer "Zhores" named after the Nobel Laureate Zhores Alferov, is intended for cutting-edge multidisciplinary research in data-driven simulations and modeling, machine learning, Big Data and artificial intelligence (AI). It enables research in such important fields as Bio-medicine [46,44], Computer Vision [20,21,11,42,45,8], Remote Sensing and Data Processing [32,34,13], Oil/Gas [33,19], Internet of Things [37,38], High Performance Computing (HPC) [29,14], Quantum Computing [10,9], Agro-informatics [32], Chemical-informatics [39,35,16,15,17] and many more. Its architecture reflects the modern trend of convergence of "traditional" HPC, Big Data and AI.…”

Section: Introductionmentioning

confidence: 99%

“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

et al. 2019

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The Petaflops supercomputer "Zhores" recently launched in the "Center for Computational and Data-Intensive Science and Engineering" (CDISE) of Skolkovo Institute of Science and Technology (Skoltech) opens up new exciting opportunities for scientific discoveries in the institute especially in the areas of data-driven modeling, machine learning and artificial intelligence. This supercomputer utilizes the latest generation of Intel and NVidia processors to provide resources for the most compute intensive tasks of the Skoltech scientists working in digital pharma, predictive analytics, photonics, material science, image processing, plasma physics and many more. Currently it places 6 th in the Russian and CIS TOP-50 (2018) supercomputer list. In this article we summarize the cluster properties and discuss the measured performance and usage modes of this scientific instrument in Skoltech.

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Satellite Imagery Analysis for Operational Damage Assessment in Emergency Situations

Cited by 33 publications

References 12 publications

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

Weakly Supervised Fine Tuning Approach for Brain Tumor Segmentation Problem

Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

“Zhores” — Petaflops supercomputer for data-driven modeling, machine learning and artificial intelligence installed in Skolkovo Institute of Science and Technology

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