Satellite Imagery Change Detection Model

@MashoodurRehman1

I am a skilled AI developer with extensive experience in satellite imagery analysis and deep learning techniques, which makes me well-suited for your Satellite Imagery Change Detection Model project. I have successfully developed and implemented models that utilize convolutional networks, such as UNet and Siamese networks, to analyze multi-spectral data for change detection tasks. My expertise includes building machine learning architectures using Python and frameworks like TensorFlow and PyTorch, aligning with your project requirements. I have a proven track record of constructing training pipelines from preprocessing to evaluation, ensuring robust and reproducible results. In previous projects, I have also generated georeferenced change masks and summary statistics effectively, providing clear insights into surface transformations such as vegetation shifts and new constructions. I am keen to discuss your project in more detail and can provide examples of similar work. Could you share more about the specific evaluation metrics you prioritize? I am ready to help create an automated workflow tailored to your needs.

@Mahihassi

With my extensive background in AI, Machine Learning and Deep Learning, particularly in edge computing and data analysis fields, I am uniquely poised to take on your Satellite Imagery Change Detection project. My work not only involves the design of machine learning models but also encompasses a comprehensive understanding of hardware and firmware systems that round out the solution. As an Electrical Engineer specializing in Embedded Systems, I have been trained to think holistically about complex projects—from system architecture and schematics to final implementation. Additionally, I have experience with diverse microcontrollers like Raspberry Pi and Arduino that relate closely to this project. My familiarity with proven deep learning frameworks such as TensorFlow and PyTorch will enable me to build and train the model according to your specific needs. Further, I can guarantee clear and detailed instructions for you to reproduce results on your own machine as well as an evaluation report showcasing the accuracy metrics you require. Lastly, in habilitating stable IoT connectivity for various industries, I've built robust systems associated with change recognition tasks like yours. My approach marries innovation with practicality; a combination that will immensely benefit your project by delivering not just a functional model but a recipe for replicability.

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in Python, Data Processing, Software Architecture, Machine Learning (ML), Remote Sensing, Deep Learning, Predictive Analytics, Time Series Forecasting, Time Series Analysis, Model Evaluation and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@WycOj

Utilizing my extensive background in data science, machine learning, and predictive analytics, I'm perfectly equipped to aid your project. My expertise in Python, TensorFlow, and PyTorch can enable me to construct the ideal AI model for your time-series remote sensing data. I have a deep understanding of various change detection techniques, specifically approaches like Siamese or UNet-based networks, which are vital for accurately identifying and quantifying alterations within your satellite imagery. One of my key strengths is not just developing reliable models but also being able to clearly explain and transfer my work to others. This means that I can not only provide you with the machine-learning layer you need but also deliver a comprehensive package that includes everything from model architecture and training pipeline to an easy-to-follow guide for reproducing our results on your own system. Moreover, my skills extend beyond just building models—I’m well-versed in data preprocessing and manipulation, essential for the initial phases of your project. I’m confident that the depth and breadth of my experience put me in an optimal position to meet—and surpass—your expectations for this project. So let’s harness the power of AI to transform your time-series collection into actionable insights!

@sufyanasmat38

I have experience with AI/ML, computer vision, PyTorch, TensorFlow, and image segmentation, including Siamese and UNet-based approaches. I can build your satellite change-detection pipeline to compare aligned multispectral images, generate georeferenced change masks and statistics, and provide reproducible training/inference scripts with evaluation on your test set.

@dissanayakepvt

Hi, I can build the change-detection pipeline in Python by pairing aligned multispectral scenes, preparing the training data, and implementing a Siamese or U-Net style model for pixelwise change masks and georeferenced outputs. I will also package the training and inference scripts with clear command-line steps and an environment file. Best regards, Sujeewa GISPromo IT Solutions

@shrec

Hi, I can build the Python-based change detection pipeline around your already corrected and aligned multispectral imagery. I’d structure it to accept two or more acquisition dates, learn meaningful temporal differences, and produce georeferenced change masks along with useful summary statistics for vegetation, construction, and other surface changes. For the modeling approach, I’d evaluate a Siamese or UNet-based architecture based on the characteristics of your imagery and available labels, rather than forcing a single model choice upfront. The training pipeline would include preprocessing, dataset preparation, validation, inference, and evaluation on your held-out test set. I’ll package the implementation with a reproducible environment, command-line instructions, inference script, and an evaluation report covering the relevant accuracy metrics. Best, Ivane

@JustinJcob

I got you! I can build the ML layer for your multi-spectral satellite time-series change detection workflow, producing georeferenced change masks, statistics, reproducible Python training/inference scripts, and an evaluation report. I’m ready to handle the full pipeline: paired-image preprocessing, Siamese/UNet-style model design, training, validation, inference, and export of clear raster/vector outputs. I’m young, a fast learner, and available 24/7 to iterate quickly. Get the demo first before you pay. A couple of key questions: Do you already have labeled change masks for training, or should the proof of concept start with unsupervised/semi-supervised change detection? Also, what format and bands are your scenes in, GeoTIFF with Sentinel/Landsat-style bands or another sensor? Let’s chat and discuss these answers so I can propose the best architecture and workflow. Kind regards, Haroon Z

@Aaleen110

The core of this job is building a model that learns "normal" in satellite imagery so it can find deviations. I'll use Python, specifically libraries like Rasterio for reading the multi-spectral data and OpenCV for image processing and comparison. For the ML part, I'll start with a U-Net architecture, which is proven for segmentation tasks like this, and train it on pairs of your images. This will directly produce georeferenced change masks. Some bidders might offer simple differencing algorithms, but those are brittle and get fooled by normal seasonal changes or minor sensor noise. I will not do that; the AI model will learn the difference between actual, significant alterations and normal variability. How do you want to handle temporal sequences longer than two images? I can adapt the U-Net or use recurrent layers if that is a requirement. I will start by setting up the data loading and preprocessing pipeline, also writing the core training loop for the U-Net on your first set of image pairs. This will give us a baseline model quickly. 8 reviews on here, everything delivered on time and on the agreed price so far, plus Preferred Freelancer status.

@mayr81

You need an AI change-detection workflow that’s already aligned to orthorectified, radiometrically corrected multispectral imagery, so the focus is the ML layer: architecture, training pipeline, and georeferenced inference. I’ll build a Python (PyTorch) model that takes two or more acquisition dates, learns normal variability, and outputs pixel-level change masks plus quantitative summaries (e.g., change area, confidence, optional class separation for vegetation/built-up patterns). The implementation will center on a proven change-detection approach (Siamese/feature-difference plus a UNet-style refinement head) and will support multi-band inputs, normalization, patch-based training, and end-to-end tiling for large scenes. Deliverables: - Model architecture + training code packaged in a reproducible repository - Inference script to generate georeferenced masks from new date pairs - Evaluation harness producing accuracy metrics on your held-out test set (segmentation scores and summary statistics) - Setup instructions with an environment file and exact command-line steps The result: an automated pipeline that flags where and when significant surface transformations occur, ready to scale across time-series acquisitions.

@AndrewJSMAN

I've built change detection pipelines on Sentinel-2 and Landsat time-series before, so this is right in my wheelhouse. I'll structure the solution around a Siamese encoder backbone (ResNet or EfficientNet) feeding a differencing layer into a UNet decoder for per-pixel classification, trained end-to-end in PyTorch with a combined focal + dice loss to handle class imbalance. The training pipeline will ingest your orthorectified multi-spectral stacks, apply standard normalization per band, and output georeferenced change masks with confidence scores; inference will run in batches across your test imagery with reproducibility locked via seed control and requirements.txt. I'll package the repo with a shell script for environment setup, a Jupyter notebook walking through the workflow, and a detailed evaluation report covering precision, recall, F1, and IoU metrics on your held-out test set. I can start immediately and outline the full architecture after you share a sample scene pair so I can validate preprocessing assumptions. Thanks, Andrii.

@HamparProd

The first step should be to verify the label structure and temporal variability before choosing the final architecture. I would build the workflow in Python/PyTorch with Rasterio/GDAL so paired multispectral scenes are normalized, tiled, trained/inferred consistently, and written back as georeferenced change masks. For a labeled dataset I would start with a Siamese/UNet-style change detector and compare it against a simpler spectral-difference baseline. This makes it possible to measure whether the model is learning real structural change rather than seasonal or radiometric variation. Evaluation would use held-out geographic data with IoU/F1/precision/recall, plus area-based summary statistics and reproducible inference settings. I can start immediately after award.

@GISspecialist23

Hi I am a GIS and remote sensing specialist with over 10 years of experience. I have done several GIS and remote sensing analysis and I will do a professional work for you