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The integration of autonomous aerial vehicles (AAVs) in agriculture has advanced precision farming by enhancing the ability to monitor and optimize agricultural plots. Object detection—critical for identifying crops, pests, and diseases–presents challenges due to data availability and varying environmental conditions. To address these challenges, we propose a Deep Learning framework tailored to agricultural contexts, utilizing domain-specific knowledge from AAV imagery. Our framework uses a knowledge base of visual features and loss values from multiple deep-learning models during the training phase to choose the most effective model for the testing phase. This approach improves model adaptability and accuracy across diverse agricultural scenarios. Evaluated on a comprehensive dataset of AAV-captured images covering various crop types and conditions, our model shows superior performance compared to state-of-the-art techniques. This demonstrates the value of integrating domain knowledge into deep learning for enhancing object detection, ultimately advancing agricultural efficiency, supporting sustainable resource management, and reducing environmental impact.

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.

This paper presents a new data compression concept, “on-board processing,†for infrared astronomy, where space observatories have limited processing resources. The proposed approach has been developed and tested for the PACS camera from the European Space Agency (ESA) mission, Herschel. Using lossy and lossless compression, the presented method offers high compression ratio with a minimal loss of potentially useful scientific data. It also provides higher signal-to-noise ratio than that for standard compression techniques. Furthermore, the proposed approach presents low algorithmic complexity such that it is implementable on the resource-limited hardware. The various modules of the data compression concept are discussed in detail.