54 research outputs found
Towards Cross-Disaster Building Damage Assessment with Graph Convolutional Networks
In the aftermath of disasters, building damage maps are obtained using change
detection to plan rescue operations. Current convolutional neural network
approaches do not consider the similarities between neighboring buildings for
predicting the damage. We present a novel graph-based building damage detection
solution to capture these relationships. Our proposed model architecture learns
from both local and neighborhood features to predict building damage.
Specifically, we adopt the sample and aggregate graph convolution strategy to
learn aggregation functions that generalize to unseen graphs which is essential
for alleviating the time needed to obtain predictions for new disasters. Our
experiments on the xBD dataset and comparisons with a classical convolutional
neural network reveal that while our approach is handicapped by class
imbalance, it presents a promising and distinct advantage when it comes to
cross-disaster generalization.Comment: 5 pages, 3 figures, submitted to IEEE IGARSS 202
The Power of Transfer Learning in Agricultural Applications: AgriNet
Advances in deep learning and transfer learning have paved the way for
various automation classification tasks in agriculture, including plant
diseases, pests, weeds, and plant species detection. However, agriculture
automation still faces various challenges, such as the limited size of datasets
and the absence of plant-domain-specific pretrained models. Domain specific
pretrained models have shown state of art performance in various computer
vision tasks including face recognition and medical imaging diagnosis. In this
paper, we propose AgriNet dataset, a collection of 160k agricultural images
from more than 19 geographical locations, several images captioning devices,
and more than 423 classes of plant species and diseases. We also introduce
AgriNet models, a set of pretrained models on five ImageNet architectures:
VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19
achieved the highest classification accuracy of 94 % and the highest F1-score
of 92%. Additionally, all proposed models were found to accurately classify the
423 classes of plant species, diseases, pests, and weeds with a minimum
accuracy of 87% for the Inception-v3 model.Finally, experiments to evaluate of
superiority of AgriNet models compared to ImageNet models were conducted on two
external datasets: pest and plant diseases dataset from Bangladesh and a plant
diseases dataset from Kashmir
The power of transfer learning in agricultural applications: AgriNet
Advances in deep learning and transfer learning have paved the way for various automation classification tasks in agriculture, including plant diseases, pests, weeds, and plant species detection. However, agriculture automation still faces various challenges, such as the limited size of datasets and the absence of plant-domain-specific pretrained models. Domain specific pretrained models have shown state of art performance in various computer vision tasks including face recognition and medical imaging diagnosis. In this paper, we propose AgriNet dataset, a collection of 160k agricultural images from more than 19 geographical locations, several images captioning devices, and more than 423 classes of plant species and diseases. We also introduce AgriNet models, a set of pretrained models on five ImageNet architectures: VGG16, VGG19, Inception-v3, InceptionResNet-v2, and Xception. AgriNet-VGG19 achieved the highest classification accuracy of 94% and the highest F1-score of 92%. Additionally, all proposed models were found to accurately classify the 423 classes of plant species, diseases, pests, and weeds with a minimum accuracy of 87% for the Inception-v3 model. Finally, experiments to evaluate of superiority of AgriNet models compared to ImageNet models were conducted on two external datasets: pest and plant diseases dataset from Bangladesh and a plant diseases dataset from Kashmir
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