Expert YOLO Data Annotator Needed: QA & Correction of 7,525 Satellite Images

@ZohaibRoy

⭐⭐⭐⭐⭐ Accurate Data Annotation for Satellite Images ❇️ Hi My Friend, I hope you're doing well. I reviewed your project requirements and see you're looking for a data annotator for satellite images. You don't need to look any further; Zohaib is here to help you! My team has completed 50+ similar projects focused on image annotation. I will ensure that all 7,525 images are reviewed and corrected for perfect accuracy, using the YOLO format you require. ➡️ Why Me? I can easily handle your project as I have 5 years of experience in data annotation, especially with satellite imagery and vegetation. My skills include meticulous reviewing, bounding box adjustments, and ensuring strict naming conventions. Additionally, I have a strong grip on tools like CVAT and LabelImg, which will enhance the quality of your dataset. ➡️ Let's have a quick chat to discuss your project in detail. I can show you samples of my previous work to demonstrate my attention to detail. Looking forward to discussing this with you in our chat. ➡️ Skills & Experience: ✅ Data Annotation ✅ YOLO Formatting ✅ Satellite Imagery ✅ Quality Assurance ✅ Bounding Box Adjustment ✅ Image Review ✅ Attention to Detail ✅ CVAT ✅ LabelImg ✅ Roboflow ✅ Error Correction ✅ Data Formatting Waiting for your response! Best Regards, Zohaib

@rabbiawebjuggler

As a skilled and meticulous data analyst, I believe I'm perfectly suited for your image annotation project. With extensive expertise in data entry, data analysis, and particularly, web scraping, I've spent countless hours organizing and cleaning up large datasets with utmost precision and efficiency. Equipped with a sharp eye for detail, I can effectively identify and correct the labeling errors in your YOLO-formatted dataset to ensure 100% accuracy for every bounding box. My experience extends beyond general data annotation; I've worked specifically with satellite imagery before, which makes me attuned to the unique challenges that come along. Moreover, I'm well-versed in working with various image annotation tools - including CVAT and LabelImg - which means that I'm already equipped with the necessary skill-set to handle your project. Let's not forget - quality assurance is crucial. With me on board, you can rest assured knowing that each milestone delivered will go through rigorous reviews on my end, guaranteeing that no mistakes similar to the ones shown in the attached samples evade my thorough scrutiny. Furthermore, by valuing timely submission without compromising quality, we can effectively push forward towards your research goal of assessing the Urban Green Space Index in Qassim and Madinah regions.

@LiveExperts

Hello, As the leader of Live Experts, I have full faith that my team and I are the best fit for your project. Our exceptional skills span across multiple relevant domains, including Computer Science, Machine learning, Data Analysis, Software Development, and Engineering. This diverse expertise allows us to deliver work of the highest quality, which is what your project demands. Our command over YOLO format and data annotation tools like CVAT, LabelImg, and Roboflow together with a meticulous eye for detail makes us unparalleled in this field. Not only can we review all 7,525 images with deep accuracy to correct any erroneous labeling but also adhere to strict naming conventions without fail. Your attachment highlighting concerns about overlap, loose boxes and inconsistent labeling has been thoroughly noted. We pride ourselves on our ability to avoid such errors as well as our responsive nature throughout the project. Furthermore, our milestone-based delivery approach ensures progress is communicated clearly and payments are released only when you are 100% satisfied. By hiring Live Experts you not only get expert contributors but also a collaborative partner who cares deeply about the quality of their work. We would be honored to put our extensive skill set to work for you on such an important project! Thanks!

@Mahihassi

As a professional Electrical Engineer with extensive experience in Computer Vision, Deep Learning, and Image Processing, I possess the necessary expertise and attention to detail required to effectively review and achieve a 100% accurate data labeling that your project necessitates. Notably, my core expertise in microcontrollers and embedded systems, coupled with my proficiency in YOLO formatting, make me uniquely positioned to handle this task with precision and efficiency. My proven capabilities in eliminating potential errors like overlapping and cluttered boxes, loose bounding boxes, and inconsistent labeling - as indicated in the attached examples - are qualities that set me apart from others. Furthermore, I have a strong track record of adhering strictly to naming conventions ensuring zero post-processing or file renaming is required. Importantly, I understand your need for high-quality outputs at each milestone and I assure you that my work will not only meet your stringent Quality Assurance assessments but exceed them. Let's kickstart the journey on this project by engaging me for the paid test batch of 100 images. By doing so, you'll be comprehensively convinced of my abilities before we proceed any further. Looking forward to being an excellent fit for this crucial project!

@DedicatedVA

We’ve reviewed your sample images and fully understand the annotation challenges—particularly around sparse desert vegetation, dense canopy separation, and eliminating loose or inaccurate bounding boxes. Our team has strong experience working with satellite imagery in arid regions, where distinguishing actual vegetation from shadows, terrain textures, and low-contrast shrubs is critical. For your dataset, we will implement a strict two-layer QA workflow: 1. Primary Annotation Pass – Precise, tight bounding boxes for each individual canopy (no overlaps or grouping) 2. Secondary QA Review – Dedicated validation to eliminate missed detections, incorrect labels, and formatting inconsistencies We will ensure: 1. Pixel-tight bounding boxes aligned strictly to vegetation edges 2. Accurate detection of both dense clusters and sparse desert shrubs 3. Zero tolerance for shadow/terrain mislabeling 4. Perfect YOLO format compliance and exact PNG–TXT name matching 5. Fully ready-to-train output with no additional work required on your end We’re confident we can meet your 100% accuracy expectation and pass your QA benchmarks consistently across milestones. Would you like us to annotate 5–10 images from your shared samples first so you can directly compare our quality against the current annotations before starting the paid 100-image trial?

@HiraMahmood4072

Hello, With over 7 years of experience in Data Processing, Excel, Machine Learning (ML), Web Scraping, and Data Entry, I have carefully reviewed your requirement for a YOLO Data Annotator to QA & correct 7,525 satellite images. To ensure the accuracy and quality of the dataset, I will meticulously review each PNG image and corresponding YOLO TXT annotation file. I will correct any labeling errors, adjust bounding boxes for accurate vegetation capture, and strictly adhere to the YOLO format. By maintaining a strict naming convention and eliminating common errors like overlapping boxes and loose annotations, the final output will be ready-to-train without any post-processing. I am well-equipped to handle this project efficiently and effectively. Let's discuss further details in the chat. You can visit my Profile: https://www.freelancer.com/u/HiraMahmood4072 Thank you.

@zafarfarooq612

Hello, I am an experienced satellite imagery annotator specializing in vegetation datasets, with extensive YOLO-format work using CVAT and LabelImg. I understand the critical need for tight, individual bounding boxes on dense canopies, exact PNG/TXT file matching, and zero tolerance for errors. I am ready to start with the 100-image paid trial and, if approved, will deliver the remaining 7,425 images in clean, verifiable batches—100% accurate and ready for your training pipeline. Regards, Zafar

@quan9824

EXPERT in(Computer Vision and Real-time Object Detection, Counting and Tracking) Hi, how are you? I checked your detail carefully. I’ve completed the real-time people detection, counting and tracking projects before successfully. Before, using python and YOLOv8, I completed @@Pool Drowning Detection System Implementation@@ project and so on. You can check my works history on my portfolio. I am sure this field and I will do my best. I always thought "It is your job, it is also my job". Awarding me will be the fastest way to complete your task with the best rates possible. THANK YOU.

@xuezhe315

With the formidable task of cleaning and enhancing your dataset to ensure accurate Urban Green Space Index assessment, I strongly believe my skillset and experience are a great fit for your project. I have honed my computer vision skills through custom model training and deployment, which includes working with TensorFlow, PyTorch, scikit-learn, and YOLO. Thanks to this experience, I am well-versed in detecting, troubleshooting, and mitigating issues similar to those you've described. Moreover, drawing from my consistent track record in delivering complex projects at scale, I bring exceptional attention to detail as a practice rather than an afterthought. This means not only will I correct the identified errors but also endeavor to eliminate such mistakes whenever possible. Rest assured that each batch I deliver will adhere strictly to the correct naming convention and YOLO format required - removing the burden of file renaming or post-processing from your end while providing a consistently high level of expertise throughout the project's duration. Allow me the opportunity to demonstrate my value by delivering excellence in your 100-image trial batch!

@AhmadXpert2410

Hi there, I understand you need precise correction and validation of a YOLO-formatted satellite imagery dataset, ensuring every vegetation instance is accurately annotated with tight, non-overlapping bounding boxes and strict file consistency. I have experience working with computer vision datasets and annotation tools like CVAT and LabelImg, and I can systematically review each image to correct missed detections, remove false positives, and ensure every canopy is individually and cleanly captured. My approach focuses on strict quality control—carefully refining bounding boxes to eliminate loose edges, resolving overlaps in dense vegetation, and maintaining perfect alignment between image and annotation filenames. I will follow a structured workflow with validation checks to guarantee that each batch is fully compliant, consistent, and ready for immediate use in your training pipeline without any post-processing. I am comfortable starting with the 100-image paid trial and meeting your QA standards before proceeding milestone by milestone. The goal is to deliver a highly accurate, production-ready dataset that meets your zero-tolerance requirements and supports reliable model performance. Regards, Ahmad

@EhtishamHaider1

As a highly experienced data scientist and electrical engineer, with a strong background in satellite and aerial imagery analysis, I'm confident I can exceed your expectations for this project. First and foremost, my skills in MATLAB Simulink and Python - especially within the realm of computer vision and TensorFlow - are directly relevant to your requirements. With an exceptional eye for detail, I will diligently review each of the 7,525 images ensuring strict accuracy in YOLO format annotations. Rest assured, I am fully capable of achieving the 100% accuracy you require in delineating boundaries for all vegetation. Overlapping bounding boxes will be a thing of the past as I understand the importance of separating individual trees within dense vegetation. Additionally, my elaborate understanding of signal processing techniques will guarantee well-fitted bounding boxes that capture only the target green spaces without extraneous background data. Lastly, with my long experience in research and academic writing, meticulousness is integral to everything I do.

@hotanloc

Hi. I have full experiences in IMAGE ANNOTATION and VIDEO FRAME ANNOTATION. I have full experiences in huge amount image data annotation whith image labeling too for training from Yolo, and ML/DL model. I have annotated huge amount data for traing model before. I am working more than 5 yesrs in this field. I can finish your task with high quality on time. Please send me your message to discuss your project detail more...I am waiting your reply now. Thanks.

@Azursol

Hello,! I’m excited about the opportunity to help with your project. Based on your requirements, I believe my expertise in Web Scraping, Data Entry aligns perfectly with your needs. How I Will Build It: I will approach your project with a structured, goal-oriented method. Using my experience in Data Processing, Data Entry, Excel, Web Scraping, Machine Learning (ML), Image Processing, Computer Vision, Deep Learning, Image Analysis, YOLO, I’ll deliver a solution that not only meets your expectations but is also scalable, efficient, and cleanly coded. I ensure seamless integration, full responsiveness, and a strong focus on performance and user experience. Why Choose Me: - 10 years of experience delivering high-quality web and software projects - Deep understanding of Web Scraping, Data Entry and related technologies - Strong communication and collaboration skills - A proven track record — check out my freelancer portfolio. - I’m available for a call to discuss your project in more detail - Committed to delivering results on time, every time Availability: I can start immediately and complete this task within the expected timeframe. Looking forward to working with you! Best regards, Ali Zahid Saudi Arabia

@mobasherreza10

Hi there, I see that you are looking for a highly accurate data annotator to review, correct, and finalize 7,525 satellite images in YOLO format for your Urban Green Space Index research—ensuring every vegetation canopy has a tight, individual bounding box, no overlapping or loose boxes, and perfect file name matching, with a strict QA process and milestone-based payments starting with a paid 100-image test batch. I have extensive experience in satellite and aerial imagery annotation, including a recent project where I corrected and finalized over 10,000 vegetation images for a deep learning pipeline, using tools like CVAT and LabelImg to ensure each tree canopy was precisely bounded, shadows and dirt were excluded, and YOLO formatting was flawless with matching PNG/TXT pairs. I know how critical it is that the final dataset requires zero post-processing and that every batch passes rigorous spot-checks. I am ready to begin with the paid test batch of 100 images and demonstrate my attention to detail. Let's discuss the specific errors in the attached samples and your expected timeline. Best regards, Mobasher Reza

@DngPhgNam

⭐⭐⭐⭐⭐ ✅Hi there, hope you are doing well! I have extensive experience in annotating satellite and aerial imagery datasets with a focus on vegetation, ensuring precise YOLO-format annotations that directly integrate with deep learning pipelines, much like your Urban Green Space Index project. From my experience, the key to successfully completing this project is rigorous attention to bounding box accuracy and strict adherence to naming conventions to avoid any file mismatches or annotation errors. Approach: ⭕ I will first conduct a thorough review of the existing 7,525 YOLO annotations to identify and correct overlapping or loose bounding boxes. ⭕ I will ensure each tree canopy and vegetation is individually and tightly boxed with no clutter or missing labels. ⭕ Maintain strict PNG/TXT naming consistency to meet your zero post-processing requirement. ⭕ Implement a quality control checklist after each milestone to guarantee compliance with your strict QA policy. ❓ Could you please clarify the expected timeframe per milestone batch? ❓ Are there any specific tools or platforms you prefer for annotation review and submission? I am confident in delivering 100% accuracy on the paid trial batch and subsequent milestones with a focus on detail and format precision. Best regards, Nam

@codefusion53

Hi, I can meticulously review and correct your 7,525 satellite images to ensure 100% accurate YOLO annotations for your Urban Green Space Index project. I will carefully adjust, add, or remove bounding boxes so each tree, shrub, or vegetation patch is precisely captured, eliminating overlaps, loose boxes, and inconsistent labeling. File naming will be strictly maintained to match PNG and TXT files exactly, making the dataset fully ready for direct model training without additional post-processing. I recommend using LabelImg or CVAT for precise bounding-box adjustments, along with QA scripts to validate YOLO formatting and detect mismatched filenames. The workflow will start with the paid 100-image test batch, followed by milestone-based delivery of the full dataset with spot-checked QA to guarantee compliance with your zero-error standard. Best, Justin

@Friends4it

Hi, I understand you need expert QA and correction for 7,525 satellite images with YOLO annotations focused on urban green space. Your strict quality standards and zero tolerance for errors are clear, and I’m confident my expertise matches your needs. I have extensive experience with YOLO annotation tools like CVAT and LabelImg, carefully refining bounding boxes to avoid overlaps and ensure precise, tight labeling of vegetation. I’ve handled satellite image datasets and guarantee 100% accuracy, proper file naming, and readiness for training without extra work. We can begin with your paid trial milestone and proceed efficiently. I recommend a quick demo of similar annotation corrections I’ve done to ensure alignment. Delivery for your full dataset can be managed within 10 days. What specific criteria or examples do you use to define a perfect bounding box for dense vegetation areas? Best regards, Muhammad

@jesica46

Hello, I hope you’re having a great day. I reviewed your project and I would be happy to assist you with your Data Analysis needs. As a professional data analyst, my goal is to transform raw data into clear and meaningful insights that help clients understand their data and make better, data-driven decisions. I can help you clean and organize raw or unstructured data, perform accurate and detailed analysis, identify trends and patterns, and create professional charts, graphs, and dashboards. I will also provide a clear, well-structured report with actionable insights so that the results are easy to understand and useful for decision-making. I have experience working with tools such as Microsoft Excel, Google Sheets, Python, and Power BI, which allow me to analyze data efficiently and present the results in a professional and easy-to-understand format. I always focus on delivering high-quality and accurate work, maintaining clear communication with clients, ensuring fast and on-time delivery, and providing complete client satisfaction. I would love to learn more about your project. Could you please share the dataset and let me know what type of analysis or insights you are looking for? Once I review the details, I can start working immediately and deliver the results as quickly and accurately as possible. Thank you for your time and consideration. I look forward to working with you. Best regards,

@FaizalShaik80

Hello, I can deliver what you need. I went through your project details and found that I worked on almost the exact same task about two months ago. I am a skilled freelancer with 6+ years of experience in Excel, Web Scraping and I can deliver the results as quickly as possible. Feel free to visit my profile to check latest work and feedback from clients. Looking forward to working with you, connect in chat. Warm regards.