deep-learning

Fine-tuning a Large Language Models (LLMs) for satellite imagery requires a deep understanding of both theoretical concepts and practical techniques. This blog post will guide you through the process, incorporating mathematical formulations and theoretical explanations to provide a comprehensive view of the fine-tuning and deployment process.

We are pleased to announce that our paper, titled ‘Enhanced REG-Based Object Detection of Road Assets Utilizing Generalized Focal Loss A Case Study on Thai Highway Imagery’, has been accepted for oral presentation at the 5th International Conference on Highway Engineering (iCHE 2024).

A thorough examination of my journey as an AI Research Scientist, including insights into balancing academia and industry, significant publications, and guidance for future researchers.

This paper dives into the cutting-edge world of road asset detection on Thai highways, showcasing a novel approach that combines an upgraded REG model with Generalized Focal Loss. Our focus is on identifying key road elements—like pavilions, pedestrian bridges, information and warning signs, and concrete guardrails—to boost road safety and infrastructure management. While deep learning methods have shown promise, traditional models often struggle with accuracy in tricky conditions, such as cluttered backgrounds and variable lighting. To tackle these issues, we’ve integrated REG with Generalized Focal Loss, enhancing its ability to detect road assets with greater precision. Our results are impressive, the REGx model led the way with a mAP50 of 80.340, mAP50-95 of 60.840, precision of 79.100, recall of 76.680, and an F1-score of 77.870. These findings highlight the REGx model’s superior performance, demonstrating the power of advanced deep learning techniques to improve highway safety and infrastructure maintenance, even in challenging conditions.

A comprehensive exploration of generative AI, including key trends, applications, challenges, and future directions.

In this paper, we present MeViT (Medium-Resolution Vision Transformer), designed for semantic segmentation of Landsat satellite imagery, focusing on key economic crops in Thailand para rubber, corn, and pineapple. MeViT enhances Vision Transformers (ViTs) by integrating medium-resolution multi-branch architectures and revising mixed-scale convolutional feedforward networks (MixCFN) to extract multi-scale local information. Extensive experiments on a public Thailand dataset demonstrate that MeViT outperforms state-of-the-art deep learning methods, achieving a precision of 92.22%, recall of 94.69%, F1 score of 93.44%, and mean IoU of 83.63%. These results highlight MeViT’s effectiveness in accurately segmenting Thai Landsat-8 data.

My PhD thesis focuses on improving semantic segmentation of aerial and satellite images, a crucial task for applications like agriculture planning, map updates, route optimization, and navigation. Current models like the Deep Convolutional Encoder-Decoder (DCED) have limitations in accuracy due to their inability to recover low-level features and the scarcity of training data. To address these issues, I propose a new architecture with five key enhancements, a Global Convolutional Network (GCN) for improved feature extraction, channel attention for selecting discriminative features, domain-specific transfer learning to address data scarcity, Feature Fusion (FF) for capturing low-level details, and Depthwise Atrous Convolution (DA) for refining features. Experiments on Landsat-8 datasets and the ISPRS Vaihingen benchmark showed that my proposed architecture significantly outperforms the baseline models in remote sensing imagery.

Semantic segmentation of remotely-sensed aerial (or very-high resolution, VHS) images and satellite (or high-resolution, HR) images has numerous application domains, particularly in road extraction, where the segmented objects serve as essential layers in geospatial databases. Despite several efforts to use deep convolutional neural networks (DCNNs) for road extraction from remote sensing images, accuracy remains a challenge. This paper introduces an enhanced DCNN framework specifically designed for road extraction from remote sensing images by incorporating landscape metrics (LMs) and conditional random fields (CRFs). Our framework employs the exponential linear unit (ELU) activation function to improve the DCNN, leading to a higher quantity and more accurate road extraction. Additionally, to minimize false classifications of road objects, we propose a solution based on the integration of LMs. To further refine the extracted roads, a CRF method is incorporated into our framework. Experiments conducted on Massachusetts road aerial imagery and Thailand Earth Observation System (THEOS) satellite imagery datasets demonstrated that our proposed framework outperforms SegNet, a state-of-the-art object segmentation technique, in most cases regarding precision, recall, and F1 score across various types of remote sensing imagery.

In this paper, we introduce an improved deep convolutional encoder-decoder network (DCED) for segmenting road objects from aerial images. Enhancements include the use of ELU (exponential linear unit) instead of ReLU, dataset augmentation with incrementally-rotated images to increase training data by eight times, and the use of landscape metrics to remove false road objects. Tested on the Massachusetts Roads dataset, our method outperformed the SegNet benchmark and other baselines in precision, recall, and F1 scores.