Sentinel Tropical Deforestation Monitoring

@GISspecialist23

I am a GIS specialist with over 10 years of experience. I have done a lot of similar work and I am very vast in Google Earth engine. I will do a professional work for you

@MrBimbola

With my extensive experience in geospatial analysis and remote sensing, I am eminently suited for the Tropical Deforestation Monitoring project. Over my 9+ years, I've become well-versed in leveraging the benefits of tools such as ArcGIS, QGIS, Google Earth Engine, GeoServer, PostgreSQL/PostGIS, and others to analyze spatial data, conduct forest monitoring using satellite imagery (such as Landsat and Sentinel) and create meaningful visualizations. Additionally, my proficiency with Python allows me to automate complex workflows effectively, ensuring efficient and reproducible outcomes - this aligns precisely with your project's acceptance criteria. Specifically, this project is an excellent match for my expertise in change detection algorithms; I have a profound understanding of how to distinguish new clear-cuts from normal seasonal changes or spectral noise. Moreover, my ability to manage cloud platforms like Google Earth Engine will be an asset in acquiring and preprocessing the latest Sentinel imagery for your specified areas of interest. Adding on, my comprehensive web development skills will provide you with an intuitive dashboard or web map for visualizing the forest loss polygons- offering you a convenient way to understand and assess the date, area statistics, and confidence scores of the loss.

@lalitdeveloper7

Hi I can build a reproducible workflow that uses Sentinel satellite imagery to detect and track near-real-time forest loss in tropical regions, including preprocessing, automated change detection, and clear spatial outputs. The solution will deliver validated forest-loss polygons with dates and statistics, along with ready-to-run code and intuitive visualisations for quickly identifying new hotspots. Please let me know further. Thanks

@abbas021

Hello , I’ve gone through your project description carefully, and I’m confident that I can handle this project efficiently. We are an expert team which have many years of experience on JavaScript, Machine Learning (ML), Geospatial, Data Science, Data Visualization, Data Analysis Lets connect in chat so that We discuss further. Regards

@yashvardhans17

Sentinel-2 Specialist | Automated Change Detection & GEE Workflow Hello! I can build this automated forest-monitoring workflow using Google Earth Engine (GEE) and JavaScript/Python. My approach ensures zero-cost licensing while maintaining high accuracy for tropical AOIs. My Technical Approach: Preprocessing: I will use the Sentinel-2 Level-2A (Bottom-of-Atmosphere) collection with QA60 cloud masking to ensure clean mosaics in the cloudy tropics. Detection: I will implement a Normalized Burn Ratio (NBR) or NDVI-based change detection algorithm to flag fresh clear-cuts. Output: You will receive a reproducible GEE script that generates GeoTIFFs and a simple Earth Engine App dashboard for visualization. I am ready to validate the script against your ground-truth examples to ensure the 'confidence score' meets your standards.

@Cassiusred

I can build this deforestation monitoring pipeline for you. I have experience with satellite imagery processing and change detection using Python and Google Earth Engine. Here is my approach: 1. Data Acquisition: Automated Sentinel-2 imagery retrieval via Google Earth Engine or Copernicus Open Access Hub API for your AOIs. Cloud masking using the Scene Classification Layer (SCL band) plus additional cloud probability filtering. 2. Change Detection: I would implement a dual approach. First, an NDVI-based differencing pipeline to detect rapid vegetation loss between composites. Second, a random forest classifier trained on spectral bands and indices (NDVI, NBR, NDMI) to distinguish genuine clearing from seasonal changes, cloud shadows, and spectral noise. 3. Post-Processing: Morphological filtering to clean detection masks, vectorization to shapefiles with attributes (date, area, confidence score), and GeoTIFF outputs with the classified change map. 4. Dashboard: A Leaflet or Folium-based web map showing forest-loss polygons with date filtering, area statistics, and confidence scoring. Interactive and lightweight. 5. Automation: The full pipeline packaged as a reproducible Python workflow with scheduling support for weekly runs. I will validate against your ground-truth examples and iterate until detection accuracy meets your standards. Happy to discuss your specific AOIs and timeline.

@achyuthanayakadn

I am an Information Science Engineer and current Intern at KSRSAC (Karnataka State Remote Sensing Applications Centre), specializing in automated geospatial pipelines. I have extensive experience in Google Earth Engine (GEE) for large-scale land cover classification and remote sensing analysis. Proposed Workflow: Acquisition: Automated Sentinel-1 (SAR) and Sentinel-2 (Optical) data fetching via GEE to ensure monitoring even during tropical cloud cover. Processing: Implementation of robust cloud masking (QA60/s2cloudless) and atmospheric correction pipelines to ensure seamless mosaicking. Change Detection: I will utilize a Random Forest or SVM-based supervised classification routine, leveraging my prior work in GEE-based LULC studies to distinguish clearing from seasonal noise. Outputs: Automated generation of GeoJSON/Shapefile polygons and a lightweight GEE App dashboard for real-time visualization. Relevant Experience: LULC Classification: Conducted a comparative study of supervised vs. unsupervised classification using GEE. KSRSAC Internship: Actively working on vector mapping and geospatial feature extraction for state-level applications. AI Integration: Developed high-accuracy diagnostic pipelines (92% accuracy) using YOLOv5, which can be adapted for rapid hotspot flagging. I can provide a fully reproducible, open-source Python/GEE API solution without any paid license requirements. Ready to start the pilot immediately.

@portobell

I have been building a similar project to detect pornography, violent and nsfw content into many websites to be used as a AI-script bot who moderates contents. I build the database using information regarding body measurements and and other patterns needed to identify the, in the case of my project, prohibited images. In this case I would adapt the database, backend and script to your scenario. I can show you a video or live share my screen with my actual in development nsfw-detection software, we can even add more details in this project of yours after I start to develop and see plausible features to be add. the backend is python, the frontend is a browser script, but I can adapt it for you the way you need it if its for a local software. Anyways, I put 30 days in the delivery because it depends on what we are going to be doing. Scope Implementation: -Automated acquisition of latest Sentinel-2 imagery -Cloud masking (S2 QA bands / s2cloudless), atmospheric correction, and mosaicking -Change detection using spectral indices (NDVI, NBR) and temporal differencing -Noise reduction to separate true clear-cuts from seasonal variation Deliverables: -Forest-loss polygons (shapefiles) with date, area stats, and confidence scores -GeoTIFF outputs -Lightweight dashboard or web map (GEE App or Leaflet-based) -Fully commented, reproducible workflow -README with setup and execution instructions