AI/ML Forest Species Identification via Remote Sensing

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@akshayloya55

Hi, I’m Akshay Loya, a geospatial technology professional working in GIS, Remote Sensing and GeoAI. We’ve worked on similar geospatial AI workflows where satellite/aerial imagery is processed to detect and classify real-world features and then converted into usable GIS datasets. What caught my attention here is that this is not simply a tree-detection problem. Identifying individual crowns is one part; distinguishing Sal, Mahua, Kusum, Tamarind and the other species reliably is the harder part. We’ve dealt with similar challenges in remote-sensing projects involving imagery processing, feature extraction, AI/ML-based classification and spatial validation. For this project, I would start by looking carefully at the available ground-truth data and imagery before deciding on the model. Depending on the resolution and field data, we could combine high-resolution imagery for crown detection with multispectral/time-series information for species classification. The output would ultimately be a species-wise tree inventory with location, count and confidence score, along with proper accuracy assessment. I would also keep the workflow reproducible so that once the model is validated in the initial area, it can be applied to larger parts of Jharkhand as more training data becomes available. I’d be happy to share relevant examples of our previous Remote Sensing/GeoAI work and discuss the data you already have for the 10 species. Best, Akshay

@shadabkhan92

With utmost certainty, my team and I are fit for the job you have described. We've spent years working on similar tasks, proving our expertise with remote sensing and satellite image processing, computer vision, deep learning, tree detection and species classification. The ensuring workflow you seek is well within our wheelhouse. Regarding the remote sensing aspect, we are experienced in managing high-resolution satellite imagery as well as multispectral imagery. We're also familiar with working on sentinel-2 time-series data and drone imagery which may prove beneficial for your project. And when it comes to data, we understand how crucial ground-truth or GPS data from field surveys can be; thus we're ready to assimilate these into the model development process. In terms of AI/ML Programming tools, our skill set rides the pinnacle. We are proficient in Python using PyTorch/TensorFlow frameworks, models such as DeepForest, YOLO, Mask R-CNN, U-Net, SAM (and the like). Plus we're equipped with geo-spatial knowledge using GIS/GGeoPandas/Rasterio in sync with Google Earth Engine. Therefore, we can guarantee not just a working AI/ML model but an entire workflow inclusive of individual tree/crown detection or segmentation

@dissanayakepvt

Hi, I can build the workflow from imagery to GIS-ready species maps by combining preprocessing, tree/crown detection, feature extraction, and species classification in Python. I would adapt an open-source detector such as DeepForest or YOLO, then integrate geospatial outputs with counts, confidence scores, and accuracy assessment. Best regards, Sujeewa GISPromo IT Solutions

@TeraByte26

Hi Pratib Waiting for your response in chat! Best Regards.

@albertc1996

Hi, I have hands-on experience with AI/ML, computer vision, Python, and image-based projects, and I’m particularly interested in applying these skills to this remote-sensing problem. I’d approach this as a complete pipeline: preprocessing and geospatial data preparation, individual tree/crown detection or segmentation, feature extraction, and species classification. Depending on the available imagery and ground-truth data, I can evaluate approaches such as DeepForest, YOLO, segmentation models, and multispectral features rather than assuming one model will work best. The final workflow can produce species-wise predictions with confidence scores, tree counts, GIS-ready outputs, and an accuracy assessment including a confusion matrix. I’d first evaluate the available imagery and labels to determine the most realistic approach and expected accuracy.

@mahads31

As an AI/ML and remote sensing expert, I offer a perfect blend of technical skills and industry experience that makes me the best fit for executing your project. My deep understanding of computer vision and geospatial AI, coupled with my proficiency in Python, Tensorflow, and PyTorch, makes developing or fine-tuning an AI/ML model that accurately identifies the 10 major NTFP species in Jharkhand a task well-worth entrusting to me. Throughout my career, I've leveraged remote sensing data to effectively detect and classify objects - from large scale land-cover mapping to intricate tree detection and species classification projects. I've got extensive experience using frameworks such as DeepForest, YOLO, Mask R-CNN and U-Net - all of which align perfectly with your project's needs. Additionally, having skillfully applied spectral and temporal features in various geospatial AI projects before, utilizing these for tree species mapping wouldn't be new terrain for me.

@devanshkhandekar

Hi, I am a seasoned Applied ML Engineer(6+ yoe) with hands-on geospatial AI,satellite-derived environmental analytics & production computer-vision pipeline experience. -Relevant geospatial work includes an India heat-awareness mapping platform combining satellite-derived NDVI/green-cover,Land Surface Temperature/Urban Heat Island & fire-hotspot layers for spatial analysis & map-based decision support. -For Jharkhand NTFP mapping,I would avoid relying on RGB crown appearance alone. Workflow: ortho/satellite preprocessing → crown detection/instance segmentation → GPS-linked crown labels → multispectral/temporal feature extraction → 10-species classifier → GIS inventory. -I’d benchmark DeepForest/YOLO or Mask R-CNN for crown detection,then classify crowns using RGB texture/shape plus Sentinel-2 red-edge,NIR,SWIR,NDVI/NDRE & seasonal phenology. -Key niche problem: Sal,Mahua,Kendu etc. can have overlapping canopy appearance in one season. Multi-date imagery is critical because leaf-flush/senescence/flowering timing can provide stronger separation than a single RGB scene. -I’ll spatially align field GPS,drone/high-res crowns & Sentinel pixels,while preventing train/test leakage by splitting geographically rather than randomly. -Outputs: per-tree species,confidence,coordinates/crown polygon,GeoJSON/GeoPackage,species counts,confusion matrix,reproducible PyTorch/Rasterio/GeoPandas pipeline & scaling documentation.

@alluankit01

Hi, I can develop a remote sensing + AI/ML workflow for identifying and mapping the 10 NTFP tree species in Jharkhand, including crown detection, species classification, GIS outputs, tree counts, and accuracy reporting. The best solution is to first review available imagery, resolution, study area, ground-truth/GPS samples, target species distribution, and expected output format. Then I’ll design a workflow using preprocessing, tree/crown detection or segmentation, spectral/texture/temporal feature extraction, model training/fine-tuning, species classification, and GIS-ready export. I’m comfortable with Python, remote sensing, computer vision, PyTorch/TensorFlow, DeepForest/YOLO/Mask R-CNN/U-Net-style models, GeoPandas, Rasterio, satellite/drone imagery, Sentinel-2 time series, GIS mapping, accuracy assessment, and confusion matrix reporting. Deliverables will include: * AI/ML species identification workflow * Image preprocessing pipeline * Tree/crown detection or segmentation * Classification for 10 target species * Species-wise tree count * GIS-ready Shapefile/GeoJSON/GeoPackage * Confidence score per prediction * Accuracy assessment * Confusion matrix * Source code and documentation * Scaling recommendations for Jharkhand I’ll focus on building a practical, reproducible model pipeline based on the available imagery and ground-truth data, with clear methodology and realistic accuracy reporting. Best regards Ankit

@Cruz1217

Distinguishing Sal from Mahua in Jharkhand often fails when the model only looks at raw RGB bands. I'll start by fusing Sentinel-2 time series with high-resolution drone patches, then fine-tune a DeepForest backbone using spectral and crown shape features. The pipeline will run in PyTorch, output GeoJSON files via GeoPandas, and include a confidence score for each tree. A common mistake is training on a single season and then expecting the model to work year-round, which inflates false positives. You'll receive a species-wise inventory map, per-tree counts, and a confusion matrix that highlights any remaining mix-ups.

@larencea30

Hi, I can help develop the Python-based AI/ML pipeline for your NTFP species mapping project, with a focus on building a modular and reproducible workflow from imagery preprocessing through model inference and GIS-ready output. I have 5+ years of experience with Python, AI/API-driven applications, data processing, backend systems, databases, and production software development. I can structure the workflow around tree/crown detection, feature extraction, species classification, confidence scoring, tree counting, and export to GeoJSON/Shapefile/GeoPackage. I would approach the system as a modular pipeline so different models such as DeepForest, YOLO, segmentation models, or other suitable open-source approaches can be evaluated based on your imagery and ground-truth data rather than assuming one model will work best. I can also implement the training/inference pipeline, validation workflow, per-species metrics, confusion matrix, and reproducible configuration for scaling to larger areas. I won’t overstate specific forest-species mapping experience, so I’d first review your available imagery, ground-truth/GPS data, spatial resolution, and annotation format to determine the most reliable modeling approach for the 10 target species. I can provide clean, documented Python source code and a complete workflow suitable for further research and production scaling. Best, Larence

@Visharaa2024

Hello, I can develop a geospatial AI model to detect, classify, count, and map the 10 NTFP tree species in Jharkhand. I will preprocess satellite/drone and GPS data, detect individual crowns using DeepForest, YOLO, or SAM, classify species using spectral and temporal features, and generate GIS-ready maps with confidence scores. Deliverables will include source code, trained models, accuracy reports, documentation, and a statewide scaling plan. I recommend starting with a labelled pilot area before expanding across Jharkhand. A few questions: What imagery and ground-truth data are available? How many verified samples exist per species? Which area should the pilot cover? What accuracy level is expected? What are your timeline and budget? Best Regards VDinfotech

@Sharazkhan2020

Hi, I can help you build a Remote Sensing + AI/ML workflow to identify and map the 10 NTFP tree species in Jharkhand using satellite/drone imagery, computer vision, and GIS-based outputs. Your goal is not just classification, but a complete species inventory pipeline: individual tree/crown detection, species prediction, confidence scoring, and exportable GIS maps. I can help design and implement that workflow using suitable approaches such as DeepForest, YOLO, Mask R-CNN, U-Net, or custom deep learning models depending on available imagery and ground-truth data. 1. Analyze available satellite/drone imagery, GPS field samples, and target species characteristics. 2. Build preprocessing and feature extraction pipelines using tools like Google Earth Engine, Rasterio, GeoPandas, and spectral/temporal features. 3. Train or fine-tune tree detection and species classification models. 4. Validate results with accuracy metrics, confusion matrix, and improve performance through iteration. I have experience working with Python-based ML pipelines, computer vision models, and geospatial data processing. For similar remote sensing projects, I have worked on image segmentation, object detection, and GIS-ready prediction workflows. I can deliver clean source code, training/inference pipelines, documentation, and recommendations for scaling across Jharkhand. Could we discuss the available ground-truth dataset and imagery resolution so I can suggest the most reliable modeling approach?

@InjectAI

I can develop the complete Remote Sensing and AI/ML workflow for identifying and mapping the 10 target NTFP tree species in Jharkhand. My background is in computer vision, AI/ML, YOLO based object detection, satellite imagery, image processing, and geospatial workflows. I can build the pipeline from raw imagery and ground-truth data through individual crown detection, feature extraction, species classification, and GIS-ready mapping. Proposed workflow: • Satellite/drone/multispectral imagery preprocessing • Individual tree and crown detection/segmentation • Spectral, temporal, textural, and crown-level feature extraction • Deep learning based species classification for the 10 NTFP species • Confidence score for every prediction • Species-wise tree counting and spatial inventory • GeoJSON/Shapefile/GeoPackage outputs • Accuracy assessment, confusion matrix, and per-class metrics • Reproducible training and inference pipeline • Recommendations for scaling across Jharkhand Depending on the available ground-truth data and imagery resolution, I can evaluate suitable approaches such as DeepForest, YOLO, U-Net, Mask R-CNN, SAM, or a custom PyTorch model rather than forcing one model onto the problem. Deliverables: • Complete source code • Preprocessing and training pipeline • Trained/fine-tuned model • Species-wise GIS inventory • Accuracy and validation report • Documentation and inference workflow

@ronakkataria1

Your deliverable list contains a conflict that decides the whole project, so I would settle it before anyone quotes. You want individual tree detection and a species-wise tree count, and you list Sentinel-2 among the data sources. Those do not go together. At 10m a single Sentinel pixel spans several crowns, so Sentinel gives you species-dominant stand mapping, not trees you can count. Counting individual trees needs sub-metre, so drone or something like Pleiades or WorldView. Both are legitimate outputs and both are useful for NTFP planning, they are just different projects with different data costs. Which imagery you actually have decides whether the tree count deliverable is even reachable. Second, on DeepForest. It delineates crowns, it does not identify species, since it was trained for generic tree detection. So this is necessarily two stages: crown detection, then a separate classifier on the extracted crowns. Worth knowing when you read the other proposals. Third, and this is what will actually make ten broadleaf species separable: phenology, not a single scene. Palash flowers bright orange around February, Mahua flowers just after, Sal and Kendu shed on their own schedule. A multi-date stack across one season pulls these apart far better than any single acquisition, and on Sentinel it is free. Question.. how many field GPS points per species do you already have? That is usually the real constraint, not the model. Ronak

@ibtemirlan

I propose a bounded feasibility milestone before committing to the full 10-species inventory. Deliverables: 1. Audit the available imagery, spatial resolution, spectral bands and GPS/ground-truth labels. 2. Prepare a reproducible Python preprocessing pipeline for one agreed sample area. 3. Train or adapt one baseline crown-detection/segmentation approach and, where labels permit, a preliminary species classifier. 4. Export predictions with confidence scores to GeoJSON and provide initial metrics/confusion matrix. 5. Document feasibility gaps, recommended data collection and a costed plan for the full system. Limit: one sample area, one baseline model and supplied data only; this milestone does not promise production accuracy, all-Jharkhand coverage or reliable classification of all 10 species. Effort: up to 15 hours. Fixed price: equivalent of 30,000 RUB, within the posted budget. Do you already have crown/species annotations with GPS coordinates? What imagery, resolution and usage rights are available?

@mohitchack255

Hi! Species-level classification for 10 phenologically distinct NTFP trees is tractable if we lean on temporal signatures, not just single-date spectral bands — Sal and Palash, for example, have very different flowering/leaf-drop timing, a strong signal beyond crown shape alone. Proposed workflow: Sentinel-2 time-series (growing-season composites via GEE) fused with high-resolution imagery for crown detail. Crown detection via DeepForest (RetinaNet), fine-tuned on your region, or Mask R-CNN/SAM if drone imagery is available. Species classification: gradient-boosted (XGBoost) or CNN model trained on spectral, textural (GLCM), and phenological time-series features per crown. Build order: 1. Data pipeline (Sentinel-2 via GEE, cloud masking, compositing) 2. Crown detection/segmentation, fine-tuned with your ground-truth GPS points 3. Feature extraction (spectral indices, texture, temporal profiles) 4. Species classifier training, validation, confusion matrix 5. GIS output (GeoPackage/Shapefile) with species, count, confidence score 6. Documentation + scaling recommendations for wider Jharkhand coverage Quick question: how many labeled ground-truth points do you have per species, and is drone imagery available for any pilot plots? Happy to share relevant tree-detection work and discuss timeline on a call.

@BhavanaSistla2k5

As an AI/ML Engineer with a focus on Python, Machine Learning, and AI, I have the skills and experience needed to take on your challenging project. My expertise extends to remote sensing, satellite image processing, computer vision, and geospatial AI - all essential for your forest species identification project. Flexibility is key in such a complex and nuanced undertaking. I am not only familiar with models like DeepForest, YOLO, Mask R-CNN and U-Net but also confident working with different data sources including high-resolution satellite imagery, multispectral imagery, Sentinel-2 time-series data as well as ground-truth/GPS data. Having worked with these diverse sources, I understand how to extract relevant features and classify the target species accurately. I am committed to delivering efficient results that tackle your specific needs. By providing you with a working AI/ML model complete with necessary source code and detailed documentation explaining the methodology employed along with potential scaling recommendations, my solutions have always been practical, scalable, user-focused and most importantly reliable underpinned by clean and maintainable code. If you entrust this project to me I will deliver more than what you expect because simplicity is consonant with complexity. Looking forward to having further discussions with you.

@utkarsh464

Hello, I’m interested in developing the Remote Sensing + AI/ML workflow for identification and mapping of the 10 major NTFP species in Jharkhand. I understand the objective is not simply image classification, but a complete geospatial pipeline: Remote sensing imagery → individual tree/crown detection → feature extraction → species classification → GIS mapping → species-wise counts and confidence scores. I can develop this as a modular Python-based workflow using appropriate computer vision, deep learning and geospatial technologies.