Azure Workflow for KML and Satellite Imagery

@ZohaibRoy

⭐⭐⭐⭐⭐ Build Azure Workflow for KML Ingestion and Satellite Imagery ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for an Azure workflow for KML ingestion and satellite imagery retrieval. You don't need to look any further; Zohaib is here to help you! My team has completed 50+ similar projects in cloud workflows. I will create an automated system to detect KML files, extract polygon data, retrieve high-resolution imagery, and store everything efficiently in Azure. ➡️ Why Me? I can easily do your Azure workflow project as I have 5 years of experience in cloud automation, specializing in Azure services, KML processing, and API integration. My expertise also covers data storage and management, ensuring a reliable and efficient solution for your needs. ➡️ Let's have a quick chat to discuss your project in detail. I can show you samples of my previous work, demonstrating my capability in this area. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Azure Logic Apps ✅ Azure Functions ✅ KML Ingestion ✅ Satellite Imagery Retrieval ✅ API Integration ✅ Data Storage Management ✅ JSON Handling ✅ Error Logging ✅ Automation Workflows ✅ Cloud Architecture ✅ Spatial Data Processing ✅ Metadata Organization Waiting for your response! Best Regards, Zohaib

@divumanocha

Hello, I am really excited about the opportunity to collaborate with you on this project! It aligns perfectly with my skill set and experience, and I’m confident I can contribute meaningfully to your vision. I genuinely enjoy working on projects like this, and I believe we can create something both functional and visually engaging. Please feel free to check out my profile to learn more about my past work and client feedback. I’d love to connect and discuss the project details further your goals, expectations, and any specific features or ideas you have in mind. The more I understand your vision, the better I can bring it to life. I am ready to get started right away and will put my full energy and focus into delivering quality results on time. My goal is not just to complete the project, but to exceed your expectations and build a long-term working relationship. Looking forward to hearing from you soon! With regards! Divya

@koolvishruth

Hi, I have 8 years of experience in same field. I have prior experience in Azure Workflow development also certified. Let’s connect

@juanjosec63

Hello, I’m excited about the opportunity to contribute to your project. With strong Azure and Python experience in event-driven workflows and geospatial processing, I can deliver a provider-agnostic pipeline that watches your chosen upload source (SharePoint/OneDrive/Blob), ingests KMLs (single/multi polygons and multi-feature files), validates WGS84 handling, extracts AOIs with centroid/area/bounds, then submits and retrieves sub-meter imagery via an interchangeable imagery-provider adapter with proper async polling, retries, and failure flags. I’ll tailor the architecture around Azure Event Grid or Logic Apps triggering Python Azure Functions (Durable Functions when needed), with Key Vault for secrets and structured Blob Storage output for original KMLs, raw/clipped GeoTIFFs, and metadata JSON per AOI, all fully logged and safe for concurrent uploads. You can expect clean, maintainable code, clear storage conventions, and a reliable handoff with deployment steps and configuration documented so you can swap imagery providers later without rewriting the core workflow. Best regards, Juan

@Solutionzhere

This is a proper geospatial ingestion pipeline, not a small script. We’ve built similar Azure-based EO workflows (KML → AOI → imagery API → structured storage) for agriculture and asset monitoring. Being direct: $30–250 is not realistic for provider-agnostic architecture, async API polling, CRS validation, reprojection, logging, and concurrent processing. Realistic estimate: $4,500 – 7,000 USD Timeline: 5–7 weeks Proposed Azure Architecture: Blob/Event Grid trigger Python Azure Functions (AOI extraction via Shapely/GeoPandas) Provider-agnostic imagery adapter (e.g., Airbus, Maxar Technologies) Durable Functions for async jobs Blob structured storage + metadata JSON Key Vault for secrets Full logging + retry logic Built scalable and ready for Phase 2 (tree detection). One key question: Do you already have a contract/API access with a specific imagery provider, or should we architect this assuming a new provider onboarding?

@mohjib092

Hi there, I will design and implement a secure, scalable Azure-based workflow to automatically ingest KML files, extract AOIs, retrieve high-resolution satellite imagery via a provider-agnostic API layer, and store GeoTIFF outputs with structured metadata. The solution will use Azure-native orchestration, secure credential management, concurrent processing, and comprehensive logging to ensure reliability and extensibility.

@trm66614

Hi there,Good afternoon I am Talha. I have read you project details i saw you need help with C# Programming, Sharepoint, API Integration, Microsoft SQL Server, Azure, Cloud Computing, Python and Geospatial I am writing to propose an innovative approach to tackle your project. Our proposal centers on delivering creative and effective solutions that will set your project apart. We will present fresh, out-of-the-box ideas that align with your project's objectives, demonstrating how we can achieve remarkable results. Please note that the initial bid is an estimate, and the final quote will be provided after a thorough discussion of the project requirements or upon reviewing any detailed documentation you can share. Could you please share any available detailed documentation? I'm also open to further discussions to explore specific aspects of the project. Thanks Regards. Talha Ramzan

@abhisaini0188

With over a decade of experience in developing web and mobile applications, I have honed my skills to deliver scalable, secure, and efficient solutions — precisely what your project requires. My proficiency in Azure, C#, Geospatial, Microsoft SQL Server and Cloud Computing uniquely positions me to develop an automated cloud workflow that will expertly handle KML ingestion, AOI processing, satellite imagery retrieval, and its storage. Drawing from my strong background in ERP system development and API integration makes me perfectly suited for this project. I am well-versed in managing cloud folders, extracting polygon geometry, image acquisition from various providers using flexible alternative acquisition modes as required. Additionally, I can successfully implement an organized data storage structure using Azure Blob Storage and JSON records for easy accessibility & analysis post-process. Furthermore, my skillset in Python scripting adds value to your project; enabling me to utilize Azure Logic App OR Event Grid triggers and Azure Function for neat processing orchestration. To conclude, I am results-driven with a keen eye on quality control and application optimization; assuring streamlined functioning of the automated workflow alongside effective logs management & error handling. Hire me and allow me to provide you with a highly performing solution tailored to your needs.

@Rekhathakur

Hello, As a highly skilled and experienced Python developer, I specialize in architecting and implementing complex systems like the one outlined in your project. My proficiency in key Microsoft Azure services makes me an ideal fit for this task. Over the years, I’ve developed a deep understanding of Azure Logic Apps, Event Grid Trigger, Azure Function (Python preferred), Durable Functions, Blob Storage, and Key Vault for credentials management among others which are essential to your project. Not only do I have a solid grasp of KML ingestion pipelines, AOI processing, satellite imagery retrieval and structured Azure storage hierarchy but my projects have demonstrated my capability to handle various complexities for diverse industries. However, one success story best aligns with this project; one where I worked on automating the data acquisition process for an agricultural company similar to yours. By successfully handling the intricacies of meeting specific API calls requirements, handling multiple uploads concurrently and orchestrating error-free operations till metadata organization; I significantly improved their efficiency and saved them valuable resources. In addition to my technical skills, my approach is always focused on delivering not only quality work but utmost client satisfaction. Let's move forward together towards transforming the objective of your idea into reality while saving cost and delivering on time. Thank you

@dtk507

Hello! I've reviewed your requirements for developing an automated Azure workflow for KML ingestion and high-resolution satellite imagery acquisition. My approach will ensure seamless detection of uploaded KML files, efficient extraction of polygon geometry, and effective retrieval of satellite imagery tailored to your specifications. I will implement a robust pipeline capable of handling multiple polygon geometries while maintaining structured storage and organized metadata in Azure. Using best practices, I’ll ensure the architecture supports future scalability and provider flexibility. Please see my portfolios for examples of similar projects I've successfully executed. Regards, Davide

@royalwebraisers

Hi, As per my understanding: you need an Azure-based pipeline for KML ingestion, AOI extraction, high-resolution imagery retrieval, and structured storage. Implementation approach: • Event-driven Azure Functions workflow • Provider-agnostic imagery integration • Blob storage + metadata logging Queries: imagery provider preference? expected KML volume? deployment timeline? I will share my portfolio on your first message.

@HiraMahmood4072

Hello, I have over 7 years of experience in Sharepoint and Python. I have carefully reviewed the requirements for the project. To accomplish the Azure Workflow for KML Ingestion and High-Resolution Satellite Imagery Acquisition, I propose the following detailed approach: 1. Develop a KML ingestion pipeline that monitors a designated cloud folder for new .kml files, extracts polygon geometry, and metadata, and handles different file structures. 2. Process each polygon to generate a buffered bounding box, prepare AOI queries, and log AOI metadata. 3. Implement a system to retrieve high-resolution satellite imagery from an API provider, supporting configurable resolution targets and various acquisition modes. 4. Store the imagery and metadata in structured Azure Blob Storage with a hierarchical organization. 5. Orchestrate the processing using Azure Logic App or Event Grid trigger, Azure Function for processing, and Key Vault for credentials management. 6. Ensure comprehensive logging and error handling mechanisms to track processing steps, failed queries, and unavailable imagery. I would be happy to discuss this proposal further in a chat. You can visit my profile at https://www.freelancer.com/u/HiraMahmood4072. Thank you.

@ianb65

Hello, there! I’m Ian Brown, a Full Stack Engineer with extensive experience designing scalable, event-driven cloud architectures. I can build a robust Azure-based workflow that automatically detects KML uploads, extracts polygon geometry and spatial metadata, prepares buffered AOIs, and retrieves sub-meter satellite imagery through a provider-agnostic integration layer. The system will use Event Grid or Logic Apps to trigger Python-based Azure Functions, with Durable Functions managing asynchronous imagery requests, polling, retries, and concurrent uploads. Imagery will be stored as GeoTIFF in structured Azure Blob Storage, alongside standardized JSON metadata including acquisition date, resolution, CRS, cloud cover, AOI metrics, and processing timestamps. Credentials will be secured in Key Vault, and comprehensive logging and failure handling will ensure production-grade reliability. My focus is clean architecture, scalability, and long-term maintainability so this pipeline becomes a solid foundation for future analytics and computer vision phases. Best regards, Ian Brown

@arbu1499

This Azure workflow is clearly structured—KML trigger → AOI extraction (area, centroid, bbox, CRS validation) → provider-agnostic imagery retrieval (≤50cm where possible) → organized Blob storage with full metadata logging. I’ll design it using Event Grid + Azure Functions (Python) with Durable Functions for async imagery jobs, secure credentials in Key Vault, and a clean abstraction layer so imagery providers can be swapped without refactoring the pipeline. The system will handle multipolygons, buffering logic, reprojection, optional clipping, structured /kml/, /imagery/, /metadata/ storage, plus robust logging and retry handling for concurrent uploads. Looking forward for your positive response in the chatbox. Best Regards, Arbaz T

@jovani81

You need an Azure-native KML ingestion pipeline that extracts AOIs and retrieves sub-meter imagery in a provider-agnostic way. I’ve built similar geospatial workflows using Azure Functions, Blob Storage, and Python (GDAL/Shapely), where the main challenge was CRS validation and async imagery polling; solved via reprojection guards and durable function orchestration. Clean architecture, concurrent-safe, fully logged, ready for scale.

@albertc1996

I'm excited to tackle the Azure Workflow for KML and Satellite Imagery project, which requires detecting uploaded .kml files, extracting polygon geometry, and obtaining high-resolution satellite imagery. The challenge lies in developing an automated cloud workflow that seamlessly integrates KML ingestion, AOI extraction, satellite imagery retrieval, and storage. I understand that the core problem is to design a scalable and efficient workflow that can handle multiple concurrent KML uploads, process large amounts of data, and provide structured storage for downstream analysis. My technical approach involves leveraging Azure Logic App or Event Grid trigger, Azure Function with Python, and Durable Functions for long-running tasks. I'll utilize libraries like Fiona for geometry extraction and Rasterio for imagery processing, ensuring a modern and efficient architecture. I've successfully developed similar workflows, including a project where I achieved a 99.9% success rate in automating satellite imagery retrieval for a large-scale agricultural monitoring system. My delivery plan includes: (1) designing the workflow architecture, (2) implementing KML ingestion and AOI extraction, and (3) integrating satellite imagery retrieval and storage. I'm confident in delivering a high-quality solution. To get started, can you please share more details about the preferred satellite imagery provider and the expected volume of KML uploads? What are the key performance indicators for this project?

@ayesha0124

Hi there, I am excited about the opportunity to develop an automated cloud workflow for KML ingestion and high-resolution satellite imagery acquisition on Azure. With extensive experience in API integration and cloud computing, I am confident in building a robust solution that meets your requirements. I have worked on similar Azure-based projects where I effectively monitored file uploads, automated data extraction, and managed spatial data workflows. I will implement a KML ingestion pipeline that efficiently processes single and multi-feature polygons, ensuring proper CRS handling and AOI metadata logging. Additionally, I will utilize a provider-agnostic architecture for satellite imagery retrieval, which allows flexibility for future changes in source providers. The entire process will leverage Azure services including Azure Functions and Blob Storage for seamless operation. I will ensure everything is well logged and any errors are handled systematically. I estimate completing the project in 14 days. Thanks,

@krisk63

Hi there, I'm Kristopher Kramer from McKinney, Texas. I’ve worked on similar projects before, and as a senior full-stack and AI engineer, I have the proven experience needed to deliver this successfully, so I have strong experience in C# Programming, Sharepoint, API Integration, Geospatial, Microsoft SQL Server, Azure, Python and Cloud Computing. I’m available to start right away and happy to discuss the project details anytime. Looking forward to speaking with you soon. Best regards, Kristopher Kramer

@devanshkhandekar

Hi,I am a seasoned Applied ML Engineer and I can can build your production Azure workflow with KML ingestion,AOI extraction,that orders sub meter satellite imagery via provider APIs, and stores everything in a clean, queryable structure for downstream analytics. Approach Trigger & Orchestration: Event Grid/Logic App->Azure Functions + Durable Functions for long-running order -> poll -> download flows,concurrent-safe queueing KML -> AOI: robust parsing (multi-polygon / multi-feature / holes), validate WGS84, compute bbox, centroid, area, configurable buffer (50–200m), emit canonical AOI payload. Provider-agnostic imagery adapter: clean interface (search/order/status/download) with one implemented provider (Planet Orders API / Maxar Ordering API) and swappable config for future providers. Processing: optional reproject + clip-to-polygon, cloud-cover/date filters . Storage & Metadata: Azure Blob hierarchy /kml/, /imagery/raw/, /imagery/clipped/, /metadata/ with JSON per AOI (provider, acquisition date, GSD, CRS, cloud cover, timestamps) Relevant experience: Built geospatial ingestion pipelines: KML/GeoJSON -> AOI normalization -> metadata catalogs -> cloud storage layouts for repeatable analytics. Integrated third-party async data APIs (submit/poll/download) with Durable orchestrations, backoff/retry, and audit logs. Delivered large imagery preprocessing stacks (COG/GeoTIFF handling, reprojection/clipping via GDAL/rasterio) optimized for cost and reliability.