Building Detection on Aerial Images Using U-NET Neural Networks

Ivanovsky, Leonid; Khryashchev, Vladimir; Pavlov, Vladimir; Ostrovskaya, Anna

doi:10.23919/fruct.2019.8711930

Cited by 35 publications

(18 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To form an objective and comprehensive evaluation of the detection performance of oil tanks, we calculate several evaluation statistics based on the detected result images, referring to the ground truth oil tanks. In accordance with the evaluation strategy adopted in recent published work in object detection [24,[32][33][34], the precision, recall, F1-measure, and Intersection-Over-Union (IOU) are calculated to evaluate the detection performance of each method. As indicated in Equations ( 3)-( 6), Precision is the percentage of detected oil tank pixels that are ground truth oil tank, Recall is the percentage of ground truth oil tank pixels that are detected, F1-measure is calculated as a general performance indicator to balance precision and recall, IOU evaluates the performance concerning the overlap ratio between detected oil tank pixels and ground truth pixels.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…In this paper, we propose an end-to-end deep neural network Res2-Unet+ to detect oil tanks with various shapes, sizes, and illumination conditions in a large-scale area based on high spatial resolution images. The network structure of Res2-Unet+ is modified from the typical network structure of Unet [23], which has been widely used for detecting various objects of interest based on remotely sensed images [24]. The main contributions of our paper are listed below:…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Chen

Wang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Oil tank inventory is significant for the economy and the military, as it can be used to estimate oil reserves. Traditional oil tank detection methods mainly focus on the geometrical characteristics and spectral features of remotely sensed images based on feature engineering. The methods have a limited application capability when the distribution pattern of ground objects in the image changes and the imaging condition varies largely. Therefore, we propose an end-to-end deep convolution network Res2-Unet+, to detect oil tanks in a large-scale area. The Res2-Unet+ method replaces the typical convolution block in the encoder of the original Unet method using hierarchical residual learning branches. A hierarchical branch is used to decompose the feature map into a few sub-channel features. To evaluate the generalization and transferability of the proposed model, we use high spatial resolution images from three different sensors in different areas to train the oil tank detection model. Images from yet another sensor in another area are used to evaluate the trained model. Three more widely used methods, Unet, Segnet, and PSPNet, are trained and evaluated for the same dataset. The experiments prove the effectiveness, strong generalization, and transferability of the proposed Res2-Unet+ method.

show abstract

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Chen

Wang

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…The accuracy of the segmentation results is usually evaluated with different metrics, e.g. Sørensen-Dice coefficient (SDC) (Ivanovsky et al 2019, Ulku et al 2020, Chhor, Aramburu and Bougdal-Lambert 2017. SDC (also known as F1 score) algorithm is a statistic to assess the area of overlap or intersection over the total number of pixels in both images.…”

Section: Experiemental Results and Discussionmentioning

confidence: 99%

Application of U-Net Convolutional Neural Network to Bushfire Monitoring in Australia With Sentinel-1/-2 Data

Lee

Trinder

Sowmya

2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

Abstract. This paper aims to define a pipeline architecture for near real-time identification of bushfire impact areas using Geoscience Australia Data Cube (AGDC). A series of catastrophic bushfires from late 2019 to early 2020 have captured international attention with their scale of devastation across four of the most populous states across Australia; New South Wales, Queensland, Victoria and South Australia. The extraction of burned areas using multispectral Sentinel-2 observations are straightforward when no cloud or haze obstruction are present. Without clear-sky observations, precisely locating the bushfire affected regions are difficult to achieve. Sentinel-1 C-band dual-polarized (VH/VV) Synthetic Aperture Radar (SAR) data is introduced to effectively elicit and analyse useful information based on backscattering coefficients, unaffected by adverse weather conditions and lack of sunlight. Burned vegetation results in significant volume scattering; co-/cross-polarised response decreases due to leafless trees, as well as coherence change over fire-disturbed areas; two sensors acquired images in a shortened revisit time over the same effected areas; all of which provided discriminative features for identifying burnt areas. Moreover, applying U-Net deep learning framework to train the recent and historical satellite data leads to an effective pre-trained segmentation model of burnt and non-burnt areas, enabling more timely emergency response, more efficient hazard reduction activities and evacuation planning during severe bushfire events. The advantages of this approach could have profound significance for a more robust, timely and accurate method of bushfire detection, utilising a scalable big data processing framework, to predict the bushfire footprint and fire spread model development.

show abstract

“…We used the U-net model architecture (Ronneberger et al, 2015). The model is a family of Convolutional Neural Network (CNN), which are FCN architectures (Long, Shelhamer, & Darrell, 2015) and was reported robust in many problems that need semantic segmentation, like medical (Ibtehaz & Rahman, 2020) and aerial (Ivanovsky, Khryashchev, Pavlov, & Ostrovskaya, 2019) image segmentation. The model mainly works with an encoder-decoder architecture where the contracting encoder extracts abstract features from an image while the expanding decoder block reconstructs segmented features (Ibtehaz & Rahman, 2020).…”

Section: The Model and Training Processmentioning

confidence: 99%

Testing Transferability of Deep-Learning-Based Dwelling Extraction in Refugee Camps

Gella¹,

Wendt²,

Lang³

et al. 2021

giforum

View full text Add to dashboard Cite

For effective humanitarian response in refugee camps, reliable information concerning dwelling type, extent, surrounding infrastructure, and respective population size is essential. As refugee camps are inherently dynamic in nature, continuous updating and frequent monitoring is time and resource-demanding, so that automatic information extraction strategies are very useful. In this ongoing research, we used labelled data and highresolution Worldview imagery and first trained a Convolutional Neural Network-based U-net model architecture. We first trained and tested the model from scratch for Al Hol camp in Syria. We then tested the transferability of the model by testing its performance in an image of a refugee camp situated in Cameroon. We were using patch size 32, at the Syrian test site, a Mean Area Intersection Over Union (MIoU) of 0.78 and F-1 score of 0.96, while in the transfer site, MIoU of 0.69 and an F-1 score of 0.98 were achieved. Furthermore, the effect of patch size and the combination of samples from test and transfer sites are investigated.

show abstract

Building Detection on Aerial Images Using U-NET Neural Networks

Cited by 35 publications

References 3 publications

Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Res2-Unet+, a Practical Oil Tank Detection Network for Large-Scale High Spatial Resolution Images

Application of U-Net Convolutional Neural Network to Bushfire Monitoring in Australia With Sentinel-1/-2 Data

Testing Transferability of Deep-Learning-Based Dwelling Extraction in Refugee Camps

Contact Info

Product

Resources

About