In an era defined by the relentless influx of data from diverse sources, the ability to harness and extract valuable insights from streaming data has become paramount. The rapidly evolving realm of online learning techniques is tailored specifically for the unique challenges posed by streaming data. As the digital world continues to generate vast torrents of real-time data, understanding and effectively utilizing online learning approaches are pivotal for staying ahead in various domains. One of the primary goals of online learning is to continuously update the model with the most recent data trends while maintaining and improving the accuracy of previous trends. Based on the various types of feedback, online learning tasks can be divided into three categories: learning with full feedback, learning with limited feedback, and learning without feedback. This survey aims to identify and analyze the key challenges associated with online learning with full feedback, including concept drift, catastrophic forgetting, skewed learning, and network adaptation, while the other existing reviews mainly focus on a single challenge or two without considering other scenarios. This article also discusses the application and ethical implications of online learning. The results of this survey provide valuable insights for researchers and instructional designers seeking to create effective online learning experiences that incorporate full feedback while addressing the associated challenges. In the end, some conclusions, remarks, and future directions for the research community are provided based on the findings of this review.

Precision in building delineation plays a pivotal role in population data analysis, city management, policy making, and disaster management. Leveraging computer vision technologies, particularly deep learning models for semantic segmentation, has proven instrumental in achieving accurate automatic building segmentation in remote sensing applications. However, current state-of-the-art (SOTA) techniques are not optimized for precisely extracting building footprints and, specifically, boundaries of the building. This deficiency highlights the need to leverage Light Detection and Ranging (LiDAR) data in conjunction with aerial RGB and streamlined deep learning for improved precision. This work utilizes the MapAI dataset, which includes a variety of objects beyond buildings, such as trees, electricity lines, solar panels, vehicles, and roads. These objects showcase diverse colors and structures, mirroring the rooftops in Denmark and Norway. Due to the aforementioned problems, this study modified UNet and CT-UNet to use LiDAR data and RGB images to segment buildings using Intersection Over Union (IoU) to evaluate building overlap and Boundary Intersection Over Union (BIoU) to evaluate precise building boundaries and shapes. The proposed work changes the configuration of these networks to streamline with LiDAR data for efficient segmentation. The batch data in training is augmented to improve model generalization and overcome overfitting. Batch normalization inclusion also improves overfitting. Four backbones with transfer learning are employed to enhance convergence and parameter efficiency of segmentation: ResNet50V2, DenseNet201, EfficientNetB4, and EfficientNetV2S. Test-Time Augmentation (TTA) is employed to improve the predicted mask. Experiments are performed using single and ensemble models, with and without Augmentation. The ensemble model outperforms the single model, and TTA also improves the results. LiDAR data with RGB improves the combined score (average of IoU and BIoU) by 13.33% compared to only RGB images.

This study investigates the application of LiDAR point cloud datasets for building segmentation through a combined approach that integrates unsupervised segmentation with evolutionary optimization. The research evaluates the extent of improvement achievable through genetic algorithm (GA) optimization for LiDAR point cloud segmentation. The unsupervised methodology encompasses preprocessing, adaptive thresholding, morphological operations, contour filtering, and terrain ruggedness analysis. A genetic algorithm was employed to fine-tune the parameters for these techniques. Critical tunable parameters, such as the interpolation method for DSM and DTM generation, scale factor for contrast enhancement, adaptive constant and block size for adaptive thresholding, kernel size for morphological operations, squareness threshold to maintain the shape of predicted objects, and terrain ruggedness index (TRI) were systematically optimized. The study presents the top ten chromosomes with optimal parameter values, demonstrating substantial improvements of 29% in the average intersection over union (IoU) score (0.775) on test datasets. These findings offer valuable insights into LiDAR-based building segmentation, highlighting the potential for increased precision and effectiveness in future applications.