Real-Time Satellite Image Analyzer for Flood Detection

₹400-750 INR / jam

Ditutup

Disiarkan 10 hari yang lalu

₹400-750 INR / jam

Developed an end-to-end deep learning system that performs pixel-level flood segmentation on satellite imagery in real time. The model accurately identifies flooded areas from Sentinel-2 or similar multispectral satellite data and generates instant flood maps — ideal for disaster response, emergency management, agriculture monitoring, and urban planning. Key Highlights & Technical Achievements: Built a UNet-ResNet34 architecture that delivers high-precision binary and multi-class segmentation on satellite images. Designed a complete AI pipeline including data preprocessing, image augmentation, model training, real-time inference, and visualization of flood masks. Deployed the model as a production-ready REST API using FastAPI, enabling instant predictions via a single API call (supports image upload and returns segmented flood map in seconds). Implemented efficient post-processing and overlay visualization so users can immediately see flooded regions overlaid on the original satellite image. Optimized the pipeline for speed and scalability, making it suitable for real-world deployment on cloud infrastructure (AWS/Azure ready). What This Project Demonstrates: Strong expertise in Computer Vision and Semantic Segmentation using modern architectures (UNet with ResNet34 backbone). Hands-on experience with satellite/remote sensing data and real-time ML deployment. Ability to take a complex AI problem from raw data → trained model → live production API. Practical skills in building solutions that solve actual environmental and disaster-related challenges. Technologies Used: PyTorch, UNet-ResNet34, FastAPI, NumPy, Matplotlib, OpenCV, Albumentations, AWS (deployment-ready) Live Demo / GitHub: [login to view URL]

Data Processing

Machine Learning (ML)

ID Projek: 40349303

Tentang projek

2 cadangan

Projek jarak jauh

Aktif 7 hari yang lalu

Ingin menjana wang?

Alamat e-mail

Faedah membida di Freelancer

Tetapkan bajet dan garis masa anda

Dapatkan bayaran untuk kerja anda

Tuliskan cadangan anda

Ianya percuma untuk mendaftar dan membida pekerjaan

2 pekerja bebas membida secara purata ₹625 INR/jam untuk pekerjaan ini

@Madnikpvtltd

Hello, I understand this is a real-time flood detection system built on satellite imagery where both segmentation accuracy and low-latency inference are critical, and my approach would be: multispectral satellite input (Sentinel-2) → preprocessing and band selection → augmentation and normalization → UNet-ResNet34 segmentation pipeline → post-processing and mask refinement → real-time inference via FastAPI → overlay visualization and flood map generation → scalable deployment (AWS/Azure) with optimized latency and throughput; I have worked on similar remote sensing and CV pipelines, including segmentation and ML-based environmental analysis, and can refine, optimize, or extend your current system further, and I can also share my previous work for reference, so if you’re aiming for a production-ready, efficient flood analysis system, let’s connect.

₹650 INR dalam 40 hari

5.0

(2 ulasan)

2.9

@PyMLExpert1

With a solid background in Computer Vision and Semantic Segmentation, specifically with satellite and remote sensing data, I have the perfect blend of skills to tackle your real-time satellite image analysis project. I've already demonstrated my ability to take complex AI problems from raw data to trained models and live production APIs through my work on the FloodSense project. This is a perfect parallel to your flood detection needs, which require not only a highly accurate algorithm but also fast, scalable deployment – a combination that I've triumphantly delivered before. My technical achievements align perfectly with the core demands of this project. Having designed a complete AI pipeline including preprocessing, image augmentation, model training, real-time inference, and visualization of flood masks – I've got you covered end-to-end! The UNet-ResNet34 model architecture I developed has proven its robustness in obtaining high-precision binary and multi-class segmentation results from satellite images. Moreover, maintaining speed without compromising quality is one of my fortes – a key requirement for real-time analysis. By leveraging FastAPI and AWS infrastructure, I ensure optimal performance even during peak usage periods. You'll be receiving production-ready results in seconds! Your project represents an opportunity for me to apply and grow my extensive skillset while making a tangible impact on disaster response.

₹600 INR dalam 96 hari