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<!DOCTYPE html> | ||
<html lang="en"> | ||
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<head> | ||
<meta http-equiv="X-UA-Compatible" content="IE=edge"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | ||
<meta charset="utf-8"> | ||
<title>Contact</title> | ||
<link rel= "stylesheet" href="style.css"> | ||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css"> | ||
<link rel="icon" type="image/png" sizes="32x32" href="icons_32.png"> | ||
</head> | ||
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<body> | ||
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<!-- Header --> | ||
<header> | ||
<h1><a class="HTMLRef" href="index.html">Michael Lambrecht</a></h1> | ||
<ul class="nav-links"> | ||
<li class="OtherPage"> <a class="HTMLRef" href="index.html">Home</a></li> | ||
<li class="OtherPage"><a class="HTMLRef" href="projects.html">Projects</a></li> | ||
<li class="CurPage"><a class="HTMLRef" href="contact.html">Contact</a></li> | ||
</ul> | ||
</header> | ||
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<h2 style="margin-top: 120px; margin-bottom: 10px">My contact details are:</h2> | ||
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<div class="contact-details"> | ||
<!-- LinkedIn --> | ||
<div class="contact-item"> | ||
<a href="https://www.linkedin.com/in/michaellambrecht2" target="_blank"> | ||
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/LinkedIn_logo_initials.png/768px-LinkedIn_logo_initials.png" alt="LinkedIn Logo"> | ||
</a>LinkedIn | ||
</div> | ||
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<!-- GitHub --> | ||
<div class="contact-item"> | ||
<a href="https://github.com/MikeyLamb" target="_blank"> | ||
<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub Logo"> | ||
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</a>GitHub | ||
</div> | ||
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<!-- Email --> | ||
<div class="contact-item"> | ||
<a href="mailto:[email protected]"> | ||
<i class="fa fa-envelope"></i> | ||
</a> | ||
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<!DOCTYPE html> | ||
<html lang="en"> | ||
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<head> | ||
<meta http-equiv="X-UA-Compatible" content="IE=edge"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | ||
<meta charset="utf-8"> | ||
<title>Michael Lambrecht</title> | ||
<link rel="stylesheet" href="style.css"> | ||
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.5.1/css/all.min.css" integrity="sha512-DTOQO9RWCH3ppGqcWaEA1BIZOC6xxalwEsw9c2QQeAIftl+Vegovlnee1c9QX4TctnWMn13TZye+giMm8e2LwA==" crossorigin="anonymous" referrerpolicy="no-referrer" /> | ||
<link rel="icon" type="image/png" sizes="32x32" href="icons_32.png"> | ||
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</head> | ||
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<body> | ||
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<!-- Header --> | ||
<header> | ||
<h1><a class="HTMLRef" href="index.html">Michael Lambrecht</a></h1> | ||
<ul class="nav-links"> | ||
<li class="CurPage"><a class="HTMLRef" href="index.html">Home</a></li> | ||
<li class="OtherPage"><a class="HTMLRef" href="projects.html">Projects</a></li> | ||
<li class="OtherPage"><a class="HTMLRef" href="contact.html">Contact</a></li> | ||
</ul> | ||
</header> | ||
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<div style="margin-top: 100px; display: flex;" class="profile-container"> | ||
<div> | ||
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<img class="profile-pic" src="profile_picture.JPEG" alt="Your Profile Picture"> | ||
</div> | ||
<div class="interests" style=""> | ||
<!-- Interests Section --> | ||
<h2 style="margin-bottom: 10px; ">Interests</h2> | ||
<ul style="list-style-type: disc;"> | ||
<li>Remote Sensing</li> | ||
<li>Machine Learning</li> | ||
<li>Geographic Information Systems</li> | ||
<li>Environmental management</li> | ||
<li>Sustainable Agriculture</li> | ||
<li>Land Use Land Cover</li> | ||
</ul> | ||
</div> | ||
</div> | ||
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<div class="about-me"> | ||
<h2>About Me</h2> | ||
<p> | ||
A dual South African and German national with a first-class MSc in the Environment and Sustainability at the University of Cape Town (UCT). Interested in applying advanced remote sensing data analysis techniques to the fields of conservation and environmental management. Skilled in using GIS software as well as data analysis using R Studio. Learning Python programming, Google Earth Engine, machine learning, and deep learning. Comfortable working in remote environments. | ||
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<h2 style="margin-top: 60px;">Published research</h2> | ||
<p>Lambrecht, M., Sowman, M. and Day, K., 2023. South Africa’s EIA Screening Tool: A preliminary study of how users perceive its accuracy and utility. Impact Assessment and Project Appraisal, 41(2), pp.102-113.</p> | ||
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<div class="contact-details"> | ||
<!-- LinkedIn --> | ||
<div class="contact-item"> | ||
<a href="https://www.linkedin.com/in/michaellambrecht2" target="_blank"> | ||
<img src="https://upload.wikimedia.org/wikipedia/commons/thumb/c/ca/LinkedIn_logo_initials.png/768px-LinkedIn_logo_initials.png" alt="LinkedIn Logo"> | ||
</a>LinkedIn | ||
</div> | ||
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<!-- GitHub --> | ||
<div class="contact-item"> | ||
<a href="https://github.com/MikeyLamb" target="_blank"> | ||
<img src="https://github.githubassets.com/images/modules/logos_page/GitHub-Mark.png" alt="GitHub Logo"> | ||
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</a>GitHub | ||
</div> | ||
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<!-- Email --> | ||
<div class="contact-item"> | ||
<a href="mailto:[email protected]"> | ||
<i class="fa fa-envelope"></i> | ||
</a> | ||
</div> | ||
</div> | ||
<!-- CV Attachment --> | ||
<a id="cv-link" href="CV.pdf" style="margin-bottom: 10px; margin-top: 50px;" download>Download CV</a> | ||
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<h2 style="margin-top: 60px; margin-bottom: 20px; text-align: center;">Hobbies</h2> | ||
<div style="display: flex; flex-wrap: wrap; justify-content: center; max-width: 400px; margin: 0 auto;"> | ||
<div style="margin: 10px; text-align: center;"> | ||
<i class="fas fa-bicycle"></i><br>Cycling | ||
</div> | ||
<div style="margin: 10px; text-align: center;"> | ||
<i class="fas fa-running"></i><br>Running | ||
</div> | ||
<div style="margin: 10px; text-align: center;"> | ||
<i class="fas fa-campground"></i><br>Camping | ||
</div> | ||
<div style="margin: 10px; text-align: center;"> | ||
<i class="fa-solid fa-mountain"></i><br>Rock climbing | ||
</div> | ||
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</body> | ||
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</html> |
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<!DOCTYPE html> | ||
<html lang="en"> | ||
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<head> | ||
<meta http-equiv="X-UA-Compatible" content="IE=edge"> | ||
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | ||
<meta charset="utf-8"> | ||
<title>Projects</title> | ||
<link rel= "stylesheet" href="style.css"> | ||
<link rel="icon" type="image/png" sizes="32x32" href="icons_32.png"> | ||
</head> | ||
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<body> | ||
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<!-- Header --> | ||
<header> | ||
<h1><a class="HTMLRef" href="index.html">Michael Lambrecht</a></h1> | ||
<ul class="nav-links"> | ||
<li class="OtherPage"> <a class="HTMLRef" href="index.html">Home</a></li> | ||
<li class="CurPage"><a class="HTMLRef" href="projects.html">Projects</a></li> | ||
<li class="OtherPage"><a class="HTMLRef" href="contact.html">Contact</a></li> | ||
</ul> | ||
</header> | ||
<h1 style=" margin-top: 120px;">LULC Machine Learning for Kilala, Kenya</h1> | ||
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<p style="width: 60%; text-align: justify; margin: 0 auto; margin-top: 20px;"> | ||
I created and ran two machine learning techniques for a farming region near the town of Kilala, a major maize growing region in Kenya. Here I used QGIS to create training input data and validation data and then used R Studio to run the SVM and RF machine learning models and to calculate the results based on the validation dataset.</p> | ||
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<!-- Image --> | ||
<img style="width:60%" src="LULC_Kilala.jpeg" alt="Your Image Description"> | ||
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<div style="margin-bottom: 50px; margin-top: 50px;" class="line"></div> | ||
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<h1>TensorFlow Binary Classification of Grape Crowing Regions in Wellington, South Africa</h1> | ||
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<p style="width: 60%; text-align: justify; margin: 0 auto; margin-top: 20px;"> | ||
As part of tutorial on Deep Learning for remote sensing I worked with a TensorFlow and a U-Net model to detect a binary class of agricultural fields in the Western Cape, South Africa. This model is rather basic using 4 Sentinel 2 bands (2, 3, 4, and 8) with a batch size of 64, data augmentation of 3 (by rotation and scaling) but with early stopping and checkpointing to avoid overfitting. The accuracy was not great with more data and layers needed to develop greater accuracy. In addition, the training data accuracy can be improved.</p> | ||
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<!-- Images side by side --> | ||
<div style="display: flex; justify-content: center; margin-top: 20px;"> | ||
<!-- Image 1 --> | ||
<div style="margin-right: 20px; text-align: center;"> | ||
<img src="Test 1.png" alt="Image 1" style="width: 250px; height: 250px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Sample tile input</p> | ||
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<div style="margin-left: 20px; text-align: center;"> | ||
<img src="Test 2.png" alt="Image 2" style="width: 250px; height: 250px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Sample tile mask</p> | ||
</div> | ||
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<p style="width: 60%; text-align: justify; margin: 0 auto;"> | ||
Below is a random sample tile showing the estimation of crop land. The area tested is a major grape growing region and so this output is an estimation of the grape growing areas in a neighbouring area. The model is not operational as the accuracy is too low. I am working on increasing the sample size to improve accuracy. | ||
</p> | ||
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<!-- Images side by side --> | ||
<div style="display: flex; justify-content: center; margin-top: 20px;"> | ||
<!-- Image 1 --> | ||
<div style="margin-right: 20px; text-align: center;"> | ||
<img src="Grape Test WC.png" alt="Image 1" style="width: 250px; height: 250px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Sample of test data</p> | ||
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<div style="margin-left: 20px; text-align: center;"> | ||
<img src="Grape Detection WC.png" alt="Image 2" style="width: 250px; height: 250px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Grape prediction</p> | ||
</div> | ||
</div> | ||
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<div style="margin-bottom: 50px; margin-top: 50px;" class="line"></div> | ||
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<h1>ResNet50 Model Classification of Satellite Images from EuroSat</h1> | ||
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<p style="width: 60%; text-align: justify; margin: 0 auto; margin-top: 20px;"> | ||
I ran 20 epochs of a ResNet50 model as an experimentation of training a model to classify satellite images. I used the EuroSat database to train and test the model. | ||
</p> | ||
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<!-- Images side by side --> | ||
<div style="display: flex; justify-content: center; margin-top: 20px;"> | ||
<!-- Image 1 --> | ||
<div style="margin-right: 20px; text-align: center;"> | ||
<img src="output.png" alt="Image 1" style="width: 300px; height: 300px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Input data visualisation</p> | ||
</div> | ||
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<div style="margin-left: 20px; text-align: center;"> | ||
<img src="Confusion.png" alt="Image 2" style="width: 300px; height: 300px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Confusion matrix</p> | ||
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<h1 style=" margin-top: 80px;">PyTorch tutorial on image classification using Neural Networks</h1> | ||
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<p style="width: 60%; text-align: justify; margin: 0 auto; margin-top: 20px;"> | ||
I followed a tutorial on applying PyTorch and a DenseNet model to classify satellite images according to four classes: water, desert, green area, and clouds. <a href="https://medium.com/analytics-vidhya/pytorch-satellite-image-classification-using-neural-networks-47f2cd1f7d82" target="_blank" style="color: blue; text-decoration: underline;">A link</a> to the tutorial can be found here. | ||
</p> | ||
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<div style="display: flex; justify-content: center; margin-top: 20px;"> | ||
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<div style="margin-right: 20px; text-align: center;"> | ||
<img src="Visual training.png" alt="Image 1" style="width: 300px; height: 300px; display: block; margin: 0 auto;"> | ||
<p style="margin-top: 10px; padding: 0;">Input data visualisation</p> | ||
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<div style="margin-left: 20px; text-align: center;"> | ||
<img src="output 2.png" alt="Image 2" style="height: 75px; display: block; margin: 0 auto;"> | ||
<img src="output 3.png" alt="Image 2" style="height: 75px; display: block; margin: 0 auto;"> | ||
<img src="output 4.png" alt="Image 2" style="height: 75px; display: block; margin: 0 auto;"> | ||
<img src="output 5.png" alt="Image 2" style="height: 75px; display: block; margin: 0 auto;"> | ||
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<p style="margin-top: 10px; padding: 0;">Output estimation</p> | ||
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<h1 style=" margin-top: 80px;">A webmap showing the mining on the West Coast of South Africa.</h1> | ||
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<p style="width: 60%; text-align: justify; margin: 0 auto; margin-top: 20px; margin-bottom: 50px;"> | ||
Current applications and approved operations for mining and oil and gas on the West Coast, South Africa Data gathered and synthesised by One Ocean Hub Research Group, Department of Environmental and Geographical Science, UCT. Disclaimer: This map is subject to constant updates based on available information. However, the accuracy of the polygons and the metadata cannot be guaranteed. | ||
Map compiled by Michael Lambrecht (November 2023).<a href="https://medium.com/analytics-vidhya/pytorch-satellite-image-classification-using-neural-networks-47f2cd1f7d82" target="_blank" style="color: blue; text-decoration: underline;">A link</a> to the tutorial can be found here. | ||
</p> | ||
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<iframe src="https://mikeylamb.github.io/RSAMiningMap/" style="width: 80%; height: 500px; border: none;"></iframe> | ||
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<div style="margin-bottom: 50px; margin-top: 50px;" class="line"></div> | ||
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</body> | ||
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