Improved image processing and added feature selection

Added feature type selection to the image processing pipeline (app.py, templates/index.html, static/js/upload.js, utils/geospatial.py, utils/segmentation.py); updated dependency management (pyproject.toml); added example notebook (attached_assets/Pasted--cells...). The `process_image_to_geojson` function now takes a `feature_type` argument. The segmentation is now more robust, able to handle various feature types.

Replit-Commit-Author: Agent
Replit-Commit-Session-Id: c7b687d7-8856-49d8-87a3-9d7f3f6499f6
Replit-Commit-Screenshot-Url: https://storage.googleapis.com/screenshot-production-us-central1/d7727d0d-3b25-49de-9476-c76c61abfa65/a2230609-9c84-43a6-8556-f1ca64f39de6.jpg

app.py

@@ -43,6 +43,10 @@ def upload_file():
     if file.filename == '':
         return jsonify({'error': 'No file selected'}), 400
     
+    # Get feature type, default to buildings if not specified
+    feature_type = request.form.get('feature_type', 'buildings')
+    logging.info(f"Processing image for feature type: {feature_type}")
+    
     # Check if the file is an allowed type
     if file and allowed_file(file.filename):
         # Generate a unique filename to prevent collisions
@@ -58,8 +62,8 @@ def upload_file():
             # Process the image
             processed_image_path = process_image(file_path, PROCESSED_FOLDER)
             
-            # Convert processed image to GeoJSON using improved processing
-            geojson_data = process_image_to_geojson(processed_image_path)
+            # Convert processed image to GeoJSON using improved processing with feature type
+            geojson_data = process_image_to_geojson(processed_image_path, feature_type=feature_type)
             
             # Save GeoJSON to file
             geojson_filename = f"{uuid.uuid4().hex}.geojson"
@@ -72,6 +76,7 @@ def upload_file():
                 'success': True,
                 'filename': unique_filename,
                 'geojson_filename': geojson_filename,
+                'feature_type': feature_type,
                 'geojson': geojson_data
             })
             

pyproject.toml

@@ -15,3 +15,1125 @@ dependencies = [
     "shapely>=2.1.0",
     "werkzeug>=3.1.3",
 ]
+
+[[tool.uv.index]]
+explicit = true
+name = "pytorch-cpu"
+url = "https://download.pytorch.org/whl/cpu"
+
+[tool.uv.sources]
+AA-module = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ABlooper = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+AnalysisG = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+AutoRAG = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+BERTeam = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+BxTorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Byaldi = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CALM-Pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+COPEX-high-rate-compression-quality-metrics = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CityLearn = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CoCa-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+CoLT5-attention = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ComfyUI-EasyNodes = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Crawl4AI = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DALL-E = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DI-toolkit = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DatasetRising = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DeepCache = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+DeepMatter = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Draugr = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ESRNN = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+En-transformer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ExpoSeq = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+FLAML = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+FSRS-Optimizer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GANDLF = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GQLAlchemy = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GhostScan = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+GraKeL = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+KapoorLabs-Lightning = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+LightAutoML = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+MMEdu = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+MRzeroCore = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Modeva = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+NeuralFoil = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+NiMARE = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+OpenHosta = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+PaLM-rlhf-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+RealtimeSTT = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+RelevanceAI-Workflows-Core = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Resemblyzer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ScandEval = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+Simba-UW-tf-dev = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+SwissArmyTransformer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+TPOT = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+TTS = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+TorchCRF = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+TotalSegmentator = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+UtilsRL = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+WhisperSpeech = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+XAISuite = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+a-unet = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+accern-xyme = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+acvl-utils = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+adabelief-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+adam-atan2-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+adan-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+adapters = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+admin-torch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+adtoolbox = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+adversarial-robustness-toolbox = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+aeiou = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+aeon = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+africanwhisper = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ag-llama-api = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+agentdojo = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+agilerl = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai-edge-torch-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai-parrot = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai-python = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai-transform = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai2-olmo = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai2-olmo-core = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ai2-tango = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+aicmder = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+aider-chat = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+aif360 = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+aimodelshare = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+airllm = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+airtestProject = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+allennlp = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+allennlp-pvt-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+apache-beam = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+apache-tvm = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+"autogluon.multimodal" = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
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+torchseg = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchserve = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchserve-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchsnapshot-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchsr = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchstain = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchsummaryX = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchtext = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchtnt = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchtnt-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchtyping = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchutil = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchvinecopulib = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchvision = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchviz = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchx = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchx-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+torchxrayvision = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+totalspineseg = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+tracebloc-package-dev = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+trainer = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transformer-engine = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transformer-lens = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transformer-smaller-training-vocab = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transformers = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transformers-domain-adaptation = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transfusion-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+transparent-background = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+treescope = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+trolo = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+tsai = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+tslearn = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ttspod = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+txtai = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+tyro = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+u8darts = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+uhg = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+uitestrunner-syberos = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ultimate-rvc = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ultralytics = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+ultralytics-thop = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unav = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unbabel-comet = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+underthesea = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unfoldNd = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unimernet = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unitorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unitxt = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unsloth = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unsloth-zoo = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unstructured = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+unstructured-inference = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+utilsd = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+v-diffusion-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vIQA = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vectice = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vector-quantize-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vectorhub-nightly = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+versatile-audio-upscaler = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vertexai = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vesin = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vgg-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+video-representations-extractor = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+viser = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vision-datasets = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+visionmetrics = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+visu3d = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vit-pytorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+viturka-nn = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vllm = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vllm-flash-attn = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vocos = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vollseg = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+vtorch = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+wavmark = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+wdoc = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+whisper-live = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+whisper-timestamped = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+whisperx = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+wilds = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+wordllama = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+worker-automate-hub = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+wxbtool = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+x-clip = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+x-transformers = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xaitk_saliency = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xformers = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xgrammar = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xinference = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+xtts-api-server = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yolo-poser = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yolov5 = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yolov7-package = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+yta-general-utils = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+zensvi = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+zetascale = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]
+zuko = [{ index = "pytorch-cpu", marker = "platform_system == 'Linux'" }]

static/js/upload.js

@@ -9,6 +9,7 @@ let currentGeoJsonFilename = null;
 // DOM elements
 const uploadForm = document.getElementById('uploadForm');
 const imageFileInput = document.getElementById('imageFile');
+const featureTypeSelect = document.getElementById('featureType');
 const processingStatus = document.getElementById('processingStatus');
 const errorMessage = document.getElementById('errorMessage');
 const resultsSection = document.getElementById('resultsSection');

templates/index.html

@@ -62,6 +62,16 @@
                                 <label for="imageFile" class="form-label">Select Image File</label>
                                 <input class="form-control" type="file" id="imageFile" accept=".png,.jpg,.jpeg,.tif,.tiff">
                             </div>
+                            <div class="mb-3">
+                                <label for="featureType" class="form-label">Feature Type to Extract</label>
+                                <select class="form-select" id="featureType" name="feature_type">
+                                    <option value="buildings" selected>Buildings</option>
+                                    <option value="trees">Trees/Vegetation</option>
+                                    <option value="water">Water Bodies</option>
+                                    <option value="roads">Roads</option>
+                                </select>
+                                <div class="form-text">Select the type of features you want to extract from the image.</div>
+                            </div>
                             <button type="submit" class="btn btn-primary">
                                 <i class="fas fa-upload"></i> Upload & Process
                             </button>

utils/geospatial.py

@@ -222,12 +222,13 @@ def convert_to_geojson_with_transform(polygons, image_height, image_width,
     
     return geojson
 
-def process_image_to_geojson(image_path):
+def process_image_to_geojson(image_path, feature_type="buildings"):
     """
     Complete pipeline to convert an image to a simplified GeoJSON.
     
     Args:
         image_path (str): Path to the processed image
+        feature_type (str): Type of features to extract ("buildings", "trees", "water", "roads")
         
     Returns:
         dict: GeoJSON object
@@ -237,29 +238,27 @@ def process_image_to_geojson(image_path):
         img = Image.open(image_path)
         width, height = img.size
         
-        # Extract contours from the image
-        polygons = extract_contours(image_path)
-        logging.info(f"Extracted {len(polygons)} initial polygons")
+        # Import segmentation module here to avoid circular imports
+        from utils.segmentation import segment_and_extract_features
+        
+        # Extract features using advanced segmentation
+        _, polygons = segment_and_extract_features(
+            image_path, 
+            output_mask_path=None,
+            feature_type=feature_type,
+            min_area=50, 
+            simplify_tolerance=2.0,
+            merge_distance=5.0
+        )
         
         if not polygons:
-            logging.warning("No polygons found in the image")
+            logging.warning("No polygons found in the image after segmentation")
             return {"type": "FeatureCollection", "features": []}
         
-        # Simplify polygons to reduce vertex count
-        polygons = simplify_polygons(polygons, tolerance=2.0)
-        logging.info(f"After simplification: {len(polygons)} polygons")
-        
-        # Regularize appropriate polygons
-        polygons = regularize_polygons(polygons)
-        
-        # Merge nearby polygons to reduce count
-        polygons = merge_nearby_polygons(polygons)
-        logging.info(f"After merging: {len(polygons)} polygons")
-        
         # Convert to GeoJSON with proper transformation
         geojson = convert_to_geojson_with_transform(
             polygons, height, width,
-            # Use generic bounds as we don't have real georeferencing
+            # Use generic bounds away from Manhattan
             min_lat=40.0, min_lon=-75.0,
             max_lat=42.0, max_lon=-73.0
         )

utils/segmentation.py

@@ -0,0 +1,237 @@
+"""
+Segmentation utilities for image processing inspired by CLIPSeg techniques.
+This is a simplified version that does not require the full transformers library.
+"""
+
+import os
+import logging
+import numpy as np
+import cv2
+from PIL import Image
+from utils.geospatial import extract_contours, simplify_polygons, regularize_polygons, merge_nearby_polygons
+
+def segment_by_color_threshold(image_path, output_path=None, 
+                              threshold=127, color_channel=1, 
+                              smoothing_sigma=1.0):
+    """
+    Segment an image based on color thresholding.
+    This is a simple segmentation inspired by more complex models like CLIPSeg.
+    
+    Args:
+        image_path (str): Path to the input image
+        output_path (str, optional): Path to save the segmentation mask
+        threshold (int): Pixel intensity threshold (0-255)
+        color_channel (int): Color channel to use for thresholding (0=R, 1=G, 2=B)
+        smoothing_sigma (float): Gaussian smoothing sigma
+        
+    Returns:
+        numpy.ndarray: Segmentation mask
+    """
+    try:
+        # Read the image
+        img = cv2.imread(image_path)
+        if img is None:
+            # Try using PIL if OpenCV fails
+            pil_img = Image.open(image_path).convert('RGB')
+            img = np.array(pil_img)
+            img = img[:, :, ::-1]  # RGB to BGR for OpenCV compatibility
+        
+        # Split channels and use the specified channel for segmentation
+        b, g, r = cv2.split(img)
+        channels = [r, g, b]
+        
+        if 0 <= color_channel < 3:
+            channel = channels[color_channel]
+        else:
+            # Use grayscale if invalid channel specified
+            channel = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        
+        # Apply Gaussian blur to reduce noise
+        if smoothing_sigma > 0:
+            channel = cv2.GaussianBlur(channel, (0, 0), smoothing_sigma)
+        
+        # Apply thresholding to create binary mask
+        _, mask = cv2.threshold(channel, threshold, 255, cv2.THRESH_BINARY)
+        
+        # Save the mask if output path is provided
+        if output_path:
+            cv2.imwrite(output_path, mask)
+            logging.info(f"Saved segmentation mask to {output_path}")
+            
+        return mask
+        
+    except Exception as e:
+        logging.error(f"Error in segmentation: {str(e)}")
+        return None
+
+def segment_by_adaptive_threshold(image_path, output_path=None, 
+                                 block_size=11, c=2, 
+                                 smoothing_sigma=1.0):
+    """
+    Segment an image using adaptive thresholding for better handling of
+    lighting variations.
+    
+    Args:
+        image_path (str): Path to the input image
+        output_path (str, optional): Path to save the segmentation mask
+        block_size (int): Size of the pixel neighborhood for threshold calculation
+        c (int): Constant subtracted from the mean
+        smoothing_sigma (float): Gaussian smoothing sigma
+        
+    Returns:
+        numpy.ndarray: Segmentation mask
+    """
+    try:
+        # Read the image
+        img = cv2.imread(image_path)
+        if img is None:
+            # Try using PIL if OpenCV fails
+            pil_img = Image.open(image_path).convert('RGB')
+            img = np.array(pil_img)
+            img = img[:, :, ::-1]  # RGB to BGR for OpenCV compatibility
+        
+        # Convert to grayscale
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        
+        # Apply Gaussian blur to reduce noise
+        if smoothing_sigma > 0:
+            gray = cv2.GaussianBlur(gray, (0, 0), smoothing_sigma)
+        
+        # Apply adaptive thresholding
+        mask = cv2.adaptiveThreshold(
+            gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, 
+            cv2.THRESH_BINARY, block_size, c
+        )
+        
+        # Save the mask if output path is provided
+        if output_path:
+            cv2.imwrite(output_path, mask)
+            logging.info(f"Saved segmentation mask to {output_path}")
+            
+        return mask
+        
+    except Exception as e:
+        logging.error(f"Error in segmentation: {str(e)}")
+        return None
+
+def segment_by_otsu(image_path, output_path=None, smoothing_sigma=1.0):
+    """
+    Segment an image using Otsu's automatic thresholding method.
+    
+    Args:
+        image_path (str): Path to the input image
+        output_path (str, optional): Path to save the segmentation mask
+        smoothing_sigma (float): Gaussian smoothing sigma
+        
+    Returns:
+        numpy.ndarray: Segmentation mask
+    """
+    try:
+        # Read the image
+        img = cv2.imread(image_path)
+        if img is None:
+            # Try using PIL if OpenCV fails
+            pil_img = Image.open(image_path).convert('RGB')
+            img = np.array(pil_img)
+            img = img[:, :, ::-1]  # RGB to BGR for OpenCV compatibility
+        
+        # Convert to grayscale
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        
+        # Apply Gaussian blur to reduce noise
+        if smoothing_sigma > 0:
+            gray = cv2.GaussianBlur(gray, (0, 0), smoothing_sigma)
+        
+        # Apply Otsu's thresholding
+        _, mask = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+        
+        # Save the mask if output path is provided
+        if output_path:
+            cv2.imwrite(output_path, mask)
+            logging.info(f"Saved segmentation mask to {output_path}")
+            
+        return mask
+        
+    except Exception as e:
+        logging.error(f"Error in segmentation: {str(e)}")
+        return None
+
+def segment_and_extract_features(image_path, output_mask_path=None, 
+                                feature_type="buildings", 
+                                min_area=50, simplify_tolerance=2.0,
+                                merge_distance=5.0):
+    """
+    Complete pipeline for segmentation and feature extraction.
+    
+    Args:
+        image_path (str): Path to the input image
+        output_mask_path (str, optional): Path to save the segmentation mask
+        feature_type (str): Type of features to extract ("buildings", "trees", "water", "roads")
+        min_area (int): Minimum feature area to keep
+        simplify_tolerance (float): Tolerance for polygon simplification
+        merge_distance (float): Distance for merging nearby polygons
+        
+    Returns:
+        tuple: (mask, polygons) - Segmentation mask and list of simplified Shapely polygons
+    """
+    # Choose segmentation method based on feature type
+    if feature_type.lower() == "buildings":
+        # Buildings typically have clean edges and good contrast
+        mask = segment_by_adaptive_threshold(
+            image_path, output_mask_path, 
+            block_size=15, c=2, smoothing_sigma=1.0
+        )
+    elif feature_type.lower() == "trees" or feature_type.lower() == "vegetation":
+        # Trees typically strong in green channel
+        mask = segment_by_color_threshold(
+            image_path, output_mask_path,
+            threshold=140, color_channel=1, smoothing_sigma=1.5
+        )
+    elif feature_type.lower() == "water":
+        # Water typically has distinct spectral properties
+        mask = segment_by_color_threshold(
+            image_path, output_mask_path,
+            threshold=120, color_channel=0, smoothing_sigma=2.0
+        )
+    else:
+        # Default to Otsu for unknown feature types
+        mask = segment_by_otsu(
+            image_path, output_mask_path, smoothing_sigma=1.0
+        )
+    
+    if mask is None:
+        logging.error("Segmentation failed")
+        return None, []
+    
+    # Save mask temporarily if needed for contour extraction
+    temp_mask_path = None
+    if not output_mask_path:
+        temp_mask_path = os.path.join(
+            os.path.dirname(image_path),
+            f"{os.path.splitext(os.path.basename(image_path))[0]}_mask.png"
+        )
+        cv2.imwrite(temp_mask_path, mask)
+        mask_path = temp_mask_path
+    else:
+        mask_path = output_mask_path
+    
+    # Extract contours from the mask
+    polygons = extract_contours(mask_path, min_area=min_area)
+    logging.info(f"Extracted {len(polygons)} initial polygons")
+    
+    # Clean up temporary file if created
+    if temp_mask_path and os.path.exists(temp_mask_path):
+        os.remove(temp_mask_path)
+    
+    # Simplify polygons
+    polygons = simplify_polygons(polygons, tolerance=simplify_tolerance)
+    
+    # If buildings, regularize them to make more rectangular
+    if feature_type.lower() == "buildings":
+        polygons = regularize_polygons(polygons)
+    
+    # Merge nearby polygons to reduce count
+    polygons = merge_nearby_polygons(polygons, distance_threshold=merge_distance)
+    logging.info(f"After processing: {len(polygons)} polygons")
+    
+    return mask, polygons