Novel Building Detection and Location Intelligence Collection in Aerial
Satellite Imagery
- URL: http://arxiv.org/abs/2302.03156v1
- Date: Mon, 6 Feb 2023 23:30:51 GMT
- Title: Novel Building Detection and Location Intelligence Collection in Aerial
Satellite Imagery
- Authors: Sandeep Singh, Christian Wiles, Ahmed Bilal
- Abstract summary: Building structures detection and information about these buildings in aerial images is an important solution for city planning and management.
It can be the center piece to answer important questions such as planning evacuation routes in case of an earthquake, flood management, etc.
- Score: 2.093287944284448
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Building structures detection and information about these buildings in aerial
images is an important solution for city planning and management, land use
analysis. It can be the center piece to answer important questions such as
planning evacuation routes in case of an earthquake, flood management, etc.
These applications rely on being able to accurately retrieve up-to-date
information. Being able to accurately detect buildings in a bounding box
centered on a specific latitude-longitude value can help greatly. The key
challenge is to be able to detect buildings which can be commercial,
industrial, hut settlements, or skyscrapers. Once we are able to detect such
buildings, our goal will be to cluster and categorize similar types of
buildings together.
Related papers
- Extracting the U.S. building types from OpenStreetMap data [0.16060719742433224]
This work creates a comprehensive dataset by providing residential/non-residential building classification covering the entire United States.
We propose and utilize an unsupervised machine learning method to classify building types based on building footprints and available OpenStreetMap information.
The validation shows a high precision for non-residential building classification and a high recall for residential buildings.
arXiv Detail & Related papers (2024-09-09T15:05:27Z) - FADE: A Dataset for Detecting Falling Objects around Buildings in Video [75.48118923174712]
Falling objects from buildings can cause severe injuries to pedestrians due to the great impact force they exert.
FADE contains 1,881 videos from 18 scenes, featuring 8 falling object categories, 4 weather conditions, and 4 video resolutions.
We develop a new object detection method called FADE-Net, which effectively leverages motion information.
arXiv Detail & Related papers (2024-08-11T11:43:56Z) - QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detection [5.886875818210989]
This letter presents the first dataset dedicated to detecting earthquake-damaged buildings from post-event very high resolution (VHR) Synthetic Aperture Radar (SAR) and optical imagery.
We deliver a dataset of coregistered building footprints and satellite image patches of both SAR and optical data, encompassing more than four thousand buildings.
arXiv Detail & Related papers (2023-12-11T18:19:36Z) - Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery.
Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
arXiv Detail & Related papers (2023-01-04T05:19:33Z) - UAV-based Visual Remote Sensing for Automated Building Inspection [15.471860216370251]
Unmanned Aerial Vehicle (UAV) based remote sensing system incorporated with computer vision has demonstrated potential for assisting building construction and in disaster management like damage assessment during earthquakes.
This paper proposes a methodology to automate these inspections through UAV-based image data collection and a software library for post-processing that helps in estimating the seismic structural parameters.
arXiv Detail & Related papers (2022-09-27T14:18:14Z) - The State of Aerial Surveillance: A Survey [62.198765910573556]
This paper provides a comprehensive overview of human-centric aerial surveillance tasks from a computer vision and pattern recognition perspective.
The main object of interest is humans, where single or multiple subjects are to be detected, identified, tracked, re-identified and have their behavior analyzed.
arXiv Detail & Related papers (2022-01-09T20:13:27Z) - Mapping Vulnerable Populations with AI [23.732584273099054]
Building functions shall be retrieved by parsing social media data like for instance tweets, as well as ground-based imagery.
Building maps augmented with those additional attributes make it possible to derive more accurate population density maps.
arXiv Detail & Related papers (2021-07-29T15:52:11Z) - Object Detection in Aerial Images: A Large-Scale Benchmark and
Challenges [124.48654341780431]
We present a large-scale dataset of Object deTection in Aerial images (DOTA) and comprehensive baselines for ODAI.
The proposed DOTA dataset contains 1,793,658 object instances of 18 categories of oriented-bounding-box annotations collected from 11,268 aerial images.
We build baselines covering 10 state-of-the-art algorithms with over 70 configurations, where the speed and accuracy performances of each model have been evaluated.
arXiv Detail & Related papers (2021-02-24T11:20:55Z) - Post-Hurricane Damage Assessment Using Satellite Imagery and Geolocation
Features [0.2538209532048866]
We propose a mixed data approach, which leverages publicly available satellite imagery and geolocation features of the affected area to identify damaged buildings after a hurricane.
The method demonstrated significant improvement from performing a similar task using only imagery features, based on a case study of Hurricane Harvey affecting Greater Houston area in 2017.
In this work, a creative choice of the geolocation features was made to provide extra information to the imagery features, but it is up to the users to decide which other features can be included to model the physical behavior of the events, depending on their domain knowledge and the type of disaster.
arXiv Detail & Related papers (2020-12-15T21:30:19Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - RescueNet: Joint Building Segmentation and Damage Assessment from
Satellite Imagery [83.49145695899388]
RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end.
RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
arXiv Detail & Related papers (2020-04-15T19:52:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.