Geospatial & Remote Sensing Projects
The project showcases the sophisticated integration of AutoEncoders architecture to tackle the intricacies of hyperspectral remote sensing data classification. Through data preprocessing and the utilization of a deep learning framework, an intricate neural network capable of discerning complex spectral patterns is constructed. Unparalleled accuracy in classifying nine distinct land cover categories is achieved by leveraging the latent space representation extracted from the trained AutoEncoders . This project not only exemplifies the technical prowess of deep learning methodologies in remote sensing applications but also heralds a new era of precise environmental monitoring and geospatial analysis.
This project employs smart techniques in satellite image segmentation using the U-Net architecture. The notebook showcases extensive preprocessing steps, including image resizing and color reclassification. With a focus on deep learning, a U-Net model is developed, featuring convolutional and pooling layers for feature extraction and down-sampling, along with transpose convolutions for up-sampling. Training involves a novel loss function combining dice and focal loss, and model performance is monitored using accuracy and Jaccard coefficient metrics, resulting in a highly accurate segmentation model. Additionally, the notebook includes visualizations of model training progress and offers custom callback functionality for further analysis and optimization.
The project utilizes deep learning techniques in Python to classify land use and land cover from remote sensing satellite data. Initial steps involve data preparation, including reading and stacking satellite bands, with ground truth labels comprising six classes such as water, plants, and bare land. Visualization aids comprehension, with RGB composite images providing insights into the data's spectral range. Data preprocessing involves creating three-dimensional patches using Principal Component Analysis (PCA), enabling input to convolutional neural networks (CNNs). The CNN architecture comprises multiple layers including convolution, dropout, and dense layers, culminating in a softmax output layer for classification. Training the model yields impressive results, with an accuracy of approximately 97%. Evaluation metrics like the confusion matrix and classification report validate the model's efficacy, while visualizations offer intuitive insights into classification maps. This project represents a significant advancement in leveraging deep learning for accurate land use classification from satellite imagery.
This project utilizes the Climate Trace CO2 emission dataset to develop a comprehensive emissions estimation model. The methodology encompasses data retrieval and summarization, exploratory and geospatial analysis, and systematic data cleaning and preprocessing. It also addresses missing data with robust imputation methods, including Iterative Imputer with Bayesian Ridge regression and MLPRegressor, ensuring data integrity and model reliability. Development of advanced machine learning and deep learning techniques are employed alongside the exploration of Explainable AI to enhance model transparency. The performance of these models is compared to optimize accuracy in emissions forecasting.
This project highlights the development of advanced machine learning models for predicting soybean yields using both tabular and remote sensing data. Initially, a detailed analysis of soybean crop data involved preprocessing techniques like normalization and missing value checks, followed by rigorous model training using ridge and Lasso regression, where hyperparameters were meticulously optimized through grid search. Further innovation is evident in the application of a Convolutional Neural Network (CNN) tailored for remote sensing data, enhanced by tuning key parameters like the number of filters and learning rates using a Hyperband search strategy. The comprehensive evaluations employed not only include regression metrics such as MSE and R2 scores but also leverage k-fold cross-validation, showcasing the model's robustness and precision in diverse conditions. This project not only demonstrates a high level of technical proficiency in handling complex datasets but also sets a benchmark in predictive analytics within agricultural data science.
This project explores a smart approach to predicting soybean yield through the fusion of tabular and raster data. Leveraging a rich array of libraries encompassing both machine learning and remote sensing techniques, the project advances agricultural modeling capabilities to new heights. Data preprocessing techniques including normalization ensure optimal model performance, while Ridge and Lasso regression algorithms deliver robust predictive outcomes. The integration of XGBoost models enhances predictive accuracy, with a meticulous analysis of regularization parameters providing insights into model stability. Visualization techniques unveil the inner workings of the models, offering profound insights into feature importance and individual tree structures. This project not only showcases technical prowess but also underscores its potential to revolutionize agricultural forecasting paradigms.