Deep Learning (DL) has become a popular method for Remote Sensing (RS) Change Detection (CD) due to its superior performance compared to traditional methods. However, generating extensive labeled datasets for DL models is time-consuming and labor-intensive. Additionally, the imbalance between changed and unchanged areas in object CD datasets, such as buildings, poses a critical issue affecting DL model efficacy. To address this issue, this paper proposes a change detection enhancement method using artificial image generation and attention mechanism. Firstly, the content of the imbalanced CD dataset is enhanced using a data augmentation strategy that synthesizes effective building CD samples using artificial RS image generation and building label creation. The created building labels, which serve as new change maps, are fed into a generator model based on a conditional Generative Adversarial Network (c-GAN) to generate high-resolution RS images featuring building changes. The generated images with their corresponding change maps are then added to the CD dataset to create the balance between changed and unchanged samples. Secondly, a channel attention mechanism is added to the proposed Adversarial Change Detection Network (Adv-CDNet) to boost its performance when training on the imbalanced dataset. The study evaluates the Adv-CDNet using WHU-CD and LEVIR-CD datasets, with WHU-CD exhibiting a higher degree of sample imbalance compared to LEVIR-CD. Training the Adv-CDNet on the augmented dataset results in a significant 16.5% F1-Score improvement for the highly imbalanced WHU-CD. Moreover, comparative analysis showcases the superior performance of the Adv-CDNet when complemented with the attention module, achieving a 6.85% F1-Score enhancement.

The progress achieved in transportation systems and artificial intelligence has amplified the use of intelligent transportation systems and Autonomous Vehicles (AVs). Indeed, AV systems have attracted much research in recent years, which enabled multiple autonomous driving tasks, including scene understanding, visual prediction, decision-making, and communication. The latter may create a bottleneck in low-resource autonomous driving systems that send the collected images to remote edge servers for processing and decision-making. Such an issue can be addressed by compressing the images in the AV and ensuring a good-quality reconstruction at the edge. In this paper, we propose a deep neural network for Compressed Sensing (CS) based image reconstruction that integrates image semantic perception to improve the reconstruction process for visual prediction tasks. The reconstruction process is optimized using a perception-inspired loss in an end-to-end model learning process. The trained model is evaluated on autonomous driving car datasets. Obtained experimental results outperform state-of-the-art approaches in terms of both image reconstruction quality and processing time. Finally, we perform semantic urban scene segmentation on the reconstructed image to evaluate reconstruction quality for visual task prediction. Obtained results on three semantic urban scene datasets demonstrate the efficiency of the proposed approach.