Como você avalia a precisão e a confiabilidade dos mapas de cobertura da terra produzidos por algoritmos de aprendizado de máquina?

As part of "Data Preparation" for any Deep Learning Project specifically in Remote Sensing, one of the biggest challenge which we have to solve is to crop massively large Satellite Imagery into multiple smaller patches. e.g. if we are performing Land Cover Segmentation on a Study Area which is as large as 10,000 sq.km. the first step would be to divide the input dataset, including both the Satellite Imagery and the Ground Truth Dataset into Small Patches of size maybe as small as 1024 x 1024 or maybe other size small or big based on the computation capacity which we have. It is usually advisable to keep the Patch Size which is divisible by 256.

In my experience, meticulous data preparation is crucial. I focus on precise calibration and validation with ground-truth data to ensure the integrity of the land cover maps I generate. This foundational work is essential for accurate machine learning outcomes.

Data preparation is crucial to produce accurate & reliable land cover maps using machine learning algorithms. Select Data: Choose remote sensing data with suitable spatial, temporal, & spectral characteristics for land cover classification. Higher spatial resolution is needed for detailed mapping. Preprocess: Before proceeding next, preprocess it to enhance its quality & rightness for analysis. Prepare Ground Truth Data: Obtain accurate ground truth data either through field surveys or existing maps. Label each pixel/sample in remote sensing data with the correct land cover class. Careful data preparation ensures machine learning algorithms are trained on high-quality, representative data, yielding accurate & reliable land cover maps.

Tech:Calculating overall accuracy, user's and producer's accuracies, and the Kappa coefficient using a confusion matrix based on ground truth data or high-resolution imagery. Cross-validation techniques and independent test sets are used to assess model generalization. Spatial and temporal consistency checks can reveal anomalies. Additionally, ensemble methods, uncertainty quantification, and comparison with existing authoritative maps provide further validation. Advanced techniques like fuzzy accuracy assessment and area-based accuracy metrics can account for mixed pixels and spatial context. Ultimately, a combination of these methods, along with domain expert review, ensures a comprehensive evaluation of the map's quality and reliability.

To ensure accuracy and reliability in machine learning-based land cover maps, a comprehensive approach is essential. Start with selecting and preprocessing high-quality remote sensing data suitable for land classification, and prepare accurate ground truth data via field surveys or validated maps. For assessment, calculate metrics like overall accuracy, user’s and producer’s accuracy, and the Kappa coefficient. Validation methods such as cross-validation, test sets, and spatial-temporal consistency checks reveal potential errors. Advanced techniques, including ensemble methods and uncertainty quantification, further enhance reliability. Finally, comparing results with authoritative sources and expert review ensures a well-rounded evaluation

In my experience, selecting the right algorithm is crucial. I balance complexity with interpretability, ensuring scalability. I recall fine-tuning a model's hyperparameters for weeks to improve accuracy, a testament to the meticulous nature of this field. Each project refines my approach, blending science with intuition.

Machine learning and confusion matrices and coefficients are all useful. But, someone eventually needs to compare the ground with the classification or know enough about the ground to help guide the classification. We assessed the accuracy of a Landsat developed map of western national forest. At the same time the map was developed there were people on site that could draw a better map from memory than any supervised or unsupervised algorithm could produce. Ground data must be included from the very beginning. Bayesian process for prior classification can help guide the rest. Until someone with the appropriate ground knowledge is brought in at the very start, all we will have are very nice machine produced speculation.

- In my role, I prioritize precision in mapping land covers, utilizing advanced algorithms to ensure data accuracy. - My approach includes rigorous validation against ground truths, enhancing the reliability of our machine learning outputs. - I advocate for continuous learning and adaptation of new methodologies to stay at the forefront of remote sensing technology. - Ethical considerations guide my practice, ensuring responsible use of the data and its implications for environmental science. - Collaboration with interdisciplinary teams enriches our analyses, leading to more nuanced and comprehensive land cover maps.

To evaluate the reliability of Land Cover maps produced by Machine Learning (ML) Algorithms, more than the Accuracy of the Prediction, it is essential to understand as to how accurate is the Ground Truth dataset used for Training a ML model. There is a strong possibility that a ML model can perform Land Cover Prediction with an Accuracy of more than 95%, but imagine a scenario where the Ground Truth dataset is 85% accurate. In that case even if the ML models perform Land Cover Segmentation with 100% accuracy, in reality the accuracy will be still 85%. Hence, understanding the accuracy of the Ground Truth dataset used for Training a ML model, can help in evaluating the true reliability of Land Cover maps produced by ML Algorithms.

You need higher quality dataset to test your model. Could be a better delineated land cover map made by expert. Compared to your prediction using confusion matrix you could see how accurate your model is. The metrics such as F1 score, ROC, and kappa index can quantify the result assement into a single number but confusion matrix still give more robust explanation per class which can useful to improve your model.

In my experience, meticulous error analysis is crucial. I cross-validate with ground truth data, adjust algorithms, and constantly refine data inputs. This iterative process enhances map reliability, reflecting my commitment to precision in remote sensing.

In my experience, rigorous validation is key. I cross-reference land cover maps with ground-truth data from various projects I've worked on, ensuring accuracy. Regular updates and peer reviews contribute to the reliability of these maps, reflecting real-world changes and maintaining integrity.

Validation and verification are after the fact. This is how cars were produced in the United States until the Japanese introduced process control and kicked the butts of the US car makers. They have W. Edwards Deming, an American to whom the American car makers wouldn't listen, to thank for their success. Currently the Toyota Camry is the most popular sedan in the United States. We must have a process control approach or we are only trying to fix our mistakes, not prevent them.