M.Phil / MS
Permanent URI for this collectionhttps://repository.cuilahore.edu.pk/handle/123456789/36
This collection archives the complete set of theses produced by students of the COMSATS University Islamabad, Lahore Campus.
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Item Wildfire Detection using Satellite Imagery(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2025) Faisal Saqib; FA22-RCS-007; Dr. Zeeshan Gillani; LHR TP 9494This thesis presents an innovative approach to wildfire detection using satellite imagery, aimed at enhancing detection accuracy and robustness through the fine-tuning of pre trained state-of-the-art deep learning models. Utilizing a dedicated wildfire satellite imagery dataset, our research focused on optimizing these models to better generalize across diverse environmental conditions encountered in satellite data. Key evaluation metrics employed in this study included Dice Loss, Intersection over Union (IoU), and Accuracy, which facilitated comprehensive performance assessments. Our method involved adjusting existing model architectures to improve their efficacy in segmenting and identifying wildfire-affected areas accurately. The results of our experiments indicate significant improvements in model performance, with increased precision in detecting and delineating wildfire boundaries. These findings not only demonstrate the potential of advanced computational techniques in the field of remote sensing for wildfire detection but also provide valuable insights for future applications in environmental monitoring and disaster management. This work underscores the effectiveness of tailored deep learning solutions in enhancing the capabilities of satellite imagery analysis for critical real-world challenges.Item Enhancing Soil Moisture Estimation through Integration of Remote Sensing and Machine Learning Techniques(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Mehwish Mukhtar; SP22-RCS-013; LHR TP 8683; Dr. Zeeshan GillaniSoil moisture (SM) dynamics are integral to effective irrigation management, influencing both crop yield and the conservation of water resources within agricultural practices. Soil Organic matter is a pivotal factor in detecting the soil moisture. The capability to accurately detect irrigation events is essential for water use optimization, a critical concern in regions facing water scarcity where judicious irrigation can bolster crop health and advance sustainable farming practices. Traditional monitoring techniques, reliant on manual field surveys and intermittent data collection, are impeded by their limited scale and lack of continuous temporal coverage. Prior research has predominantly leaned on discrete point-based measurements or episodic remote sensing data, often inadequate for the detailed, persistent monitoring that precision irrigation scheduling demands. Addressing these limitations, our study introduces a cohesive framework for enhanced irrigation detection by using the organic matter of the bare soil, over extensive corn fields by integrating high-frequency satellite imagery with advanced machine learning techniques. Our comprehensive methodology encompasses the acquisition of Sentinel-1 satellite, SMAP and SMOS data, validation with ground-truth references, and sophisticated preprocessing tactics to navigate the common temporal and spatial discrepancies encountered in remote sensing analysis. We assessed various machine learning models, notably. Linear Regression, Random Forest, SVR, and KNN, with Liner Regression demonstrating the most accurate performance in predicting irrigation events and along with other models Naïve Bayes model were used for the Bare Soil classification and Soil Organic matter Detection. Mean Square Error (MSE) and Mean Absolute Error (MAE) are the evaluation parameters for the irrigation detection and F1-Score, Precision, Recall, Support and Accuracy are the evaluation parameters for the Soil Moisture estimation. However, satellite data dependency may introduce atmospheric inconsistencies, and model adaptability across different terrains or crop types may necessitate recalibration. Future directions broader environmental parameters, exploring deep learning potentials, and model validation across diverse agricultural settings. Ultimately, our vision is to develop a universally adaptable platform that delivers real time, actionable insights for stakeholders, revolutionizing irrigation practices on a global scale Soil moisture (SM) dynamics are integral to effective irrigation management, influencing both crop yield and the conservation of water resources within agricultural practices. Soil Organic matter is a pivotal factor in detecting the soil moisture. The capability to accurately detect irrigation events is essential for water use optimization, a critical concern in regions facing water scarcity where judicious irrigation can bolster crop health and advance sustainable farming practices. Traditional monitoring techniques, reliant on manual field surveys and intermittent data collection, are impeded by their limited scale and lack of continuous temporal coverage. Prior research has predominantly leaned on discrete point-based measurements or episodic remote sensing data, often inadequate for the detailed, persistent monitoring that precision irrigation scheduling demands. Addressing these limitations, our study introduces a cohesive framework for enhanced irrigation detection by using the organic matter of the bare soil, over extensive corn fields by integrating high-frequency satellite imagery with advanced machine learning techniques. Our comprehensive methodology encompasses the acquisition of Sentinel-1 satellite, SMAP and SMOS data, validation with ground-truth references, and sophisticated preprocessing tactics to navigate the common temporal and spatial discrepancies encountered in remote sensing analysis. We assessed various machine learning models, notably. Linear Regression, Random Forest, SVR, and KNN, with Liner Regression demonstrating the most accurate performance in predicting irrigation events and along with other models Naïve Bayes model were used for the Bare Soil classification and Soil Organic matter Detection. Mean Square Error (MSE) and Mean Absolute Error (MAE) are the evaluation parameters for the irrigation detection and F1-Score, Precision, Recall, Support and Accuracy are the evaluation parameters for the Soil Moisture estimation. However, satellite data dependency may introduce atmospheric inconsistencies, and model adaptability across different terrains or crop types may necessitate recalibration. Future directions broader environmental parameters, exploring deep learning potentials, and model validation across diverse agricultural settings. Ultimately, our vision is to develop a universally adaptable platform that delivers real time, actionable insights for stakeholders, revolutionizing irrigation practices on a global scaleItem Integrating Remote Sensing and Deep Learning Techniques for Improved Crop Yield Estimation(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Umer Usman; SP22-RCS-023; LHR TP 8684; Dr. Zeeshan GillaniAccurate yield estimation is crucial for making knowledgeable choices about agriculture and food safety around the world. However, many current studies in this place face issues inclusive of neglecting soil properties as predictors, limited generalization throughout special regions, and confined use of climate variables. This study targets to fill those gaps by using the machine and deep learning and techniques to predict wheat yield in 36 districts of Punjab, Pakistan. Using a dataset created with Google Earth Engine, the dataset consists of detailed statistics on soil residences, climate and other relevant agricultural variables or flora indices. The study included an in-intensity evaluation of the dataset, followed by using training and assessment of various machine learning and deep learning techniques, which includes Artificial Neural Networks, RandomForest, GradientBoosting, and XGBoost.The findings revealed that the ANN model encountered problems together with overfitting because of limited data, while GradientBoosting and XGBoost excel in accuracy and robustness. Specifically, GradientBoosting gave an RMSE of 362.4946 and a MAE of one 101.2008 tons/district, whilst XGBoost gave an RMSE of 280.4313 and an MAE of 102.5128 tons/district. Both algorithms confirmed high R² values, indicating their effectiveness for predicting district-level productiveness. The performance of the models was evaluated with the aid of comparing their predictions with the ground reality information provided by using CRS of Punjab, with error metrics given in tons at district level. The insights gained from this study are very essential to farmers, agronomists and policy makers, allowing them to make informed decisions and promote sustainable agricultural practices. This research makes a big contribution to the sphere of yield estimation and highlights factors for future studies, such as expanding the dataset and exploring other modelling techniques including LSTM, to enhance protection of food and sustainable agricultureItem City Scale Multi Camera Vehicle Tracking Using Deep Learning(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2023) Muhammad Sameed Khan; SP20-RCS-013; LHR TP 8465; Dr. Zeeshan GillaniIntelligent city and traffic management depend on city-scale multi-camera vehicle tracking; however, this work has various difficulties. On the different viewing angles, problems arise that include the variation on the large scale, frequent occlusion and appearance variation. In this study, we use the cross-camera tracking technique and multi-camera tracking system that considers aggregation loss. To address the challenges of multi-camera vehicle tracking, the suggested system has four key parts. First, we extract the tracks with the help of a single camera view by identifying the object and multi-object tracking modules. These modules combine their detection capabilities to provide efficient tracking between frames. After obtaining the tracklets, we use a multi camera re-identification module to match them. The tracklets acquired by several cameras are connected by this module using re-identification techniques. We used OsNetX1_0 and retinaNet50 for reidentification. In the final stage, we deal with the isolated trackless and tracking of the synchronize ids that rely on the outcomes of the re-identification. We improve the computational performance with the help of less parameter models and cohesion of the tracking system by removing isolated tracklets and assuring consistent tracking IDs. With practical and effective alternatives, the research advances multi-camera vehicle tracking on the city scale. However, this system shows the significance of the fast-multi-target cross-camera tracking approaches and the loss of aggregation in the study challenges. The AI city challenges' success reveals our system's potency and viability.Item Multisource Remote Sensing Land Cover Classification for Crop Identification and Yield Prediction using Multispectral Temporal Imagery(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2021) Haseeb Rehman Khan; FA19-RCS-017; LHR TP 7593; Dr. Zeeshan GillaniRapid growths in population and climate change are resulting in food insecurity. Food security is more challenged in developing countries like Pakistan where conventional practices of crop monitoring are in place. The two aspects of crop monitoring which are crop identification and yield prediction are manual which are time-consuming and resource extensive thus resulting in delayed decision making. However, the Precision Agriculture applications for crops monitoring based on Remote Sensing data can help us to effectively monitor crops and increase production and aid decision making. This study acquires remotely sensed multispectral and multitemporal satellite imagery from Sentinel-2 mission satellites to extract spectral bands over different timestamps of the rice, wheat and sugarcane crop season. These spectral bands along with the computed vegetation indices over the growing season of wheat and rice are used with the Long Short Term Memory network for the early identification of the crops. The study also attempted to predict the yield of wheat crops with multispectral and multitemporal data. Further, the significance of the temporal data and different combinations of the spectral bands were analyzed for crop identification of the small-sized fields and a comparison was made with the existing state of the art. The best combination of the spectral bands resulted in 99.76 % accuracy for crop identification. Further, the crops are also identified with 93.77 % accuracy within the first four weeks of their seeding. The Root Means Square Error of 2. 61 and 71.67 % R-squared is reported for the prediction of the wheat yield. The crop identification accuracy with the proposed approach is suggestive of the applicability of the study for the automatic identification of crops on large scale.Multisource Remote SensingItem Computational Technique For Classification Of Membrane Proteins(Library Information Services, COMSATS University Islamabad, Lahore Campus, 2020) Harmain Asghar; SP18-RCS-012; LHR TP 5985; Dr. Zeeshan GillaniThe Cell is the most important part of the human body, many small entities and molecules are encapsulated within a cell; shielded by the cell membrane. Proteins are the basic elements within the cell that are responsible for various functionalities, like nutrients across membrane and molecule, identification of foreign bodies, etc. Membrane proteins play a very important function in predicting diseases. Due to advancements in the Next-generation Sequencing, membrane proteins dataset is ever increasing. The experimental methods for the classification of these proteins are very expensive and time-consuming. In this study, prediction about the membrane proteins and its subtypes will be made based on a computational approach using protein primary sequence and machine learning algorithm. For the purpose of more improvement we used the convolutional neural network inception v3 model. The results exhibit the effectiveness of predictive performance as compared to the other existing algorithm. Some useful techniques are described to make the study more effective.