🖼️ **模型选择与环境搭建**: 本文利用IBM开源的ResNet-50模型，并在Google Colab上搭建环境，安装PyTorch及其相关库，用于图像处理和可视化。

⚙️ **图像预处理**: 采用标准预处理流程，包括调整图像大小、裁剪、转换为张量，并进行归一化处理，以满足ResNet-50的输入要求。

🔥 **图像分类与结果可视化**: 通过从URL获取卫星图像，使用预训练的ResNet-50模型进行分类，并可视化图像及其预测结果。同时，文章还输出了前五个预测及其概率。

🚀 **灾害评估应用**: 文章以野火相关的卫星图像为例，展示了如何利用该工具进行快速的灾害评估和响应，强调了该方法在实际应用中的价值。

💡 **总结与未来展望**: 总结了利用ResNet-50模型在灾害评估中的应用，并强调了预训练CNN在解决实际问题中的潜力和便捷性。

In this tutorial, we explore an innovative and practical application of IBM’s open-source ResNet-50 deep learning model, showcasing its capability to classify satellite imagery for disaster management rapidly. Leveraging pretrained convolutional neural networks (CNNs), this approach empowers users to swiftly analyze satellite images to identify and categorize disaster-affected areas, such as floods, wildfires, or earthquake damage. Using Google Colab, we’ll walk through a step-by-step process to easily set up the environment, preprocess images, perform inference, and interpret results.

First, we install essential libraries for PyTorch-based image processing and visualization tasks.

!pip install torch torchvision matplotlib pillow

We import necessary libraries and load the pretrained IBM-supported ResNet-50 model from PyTorch, preparing it for inference tasks.

import torchimport torchvision.models as modelsimport torchvision.transforms as transformsfrom PIL import Imageimport requestsfrom io import BytesIOimport matplotlib.pyplot as pltmodel = models.resnet50(pretrained=True)model.eval()

Now, we define the standard preprocessing pipeline for images, resizing and cropping them, converting them into tensors, and normalizing them to match ResNet-50’s input requirements.

preprocess = transforms.Compose([    transforms.Resize(256),    transforms.CenterCrop(224),    transforms.ToTensor(),    transforms.Normalize(        mean=[0.485, 0.456, 0.406],        std=[0.229, 0.224, 0.225]    )])

Here, we retrieve a satellite image from a given URL, preprocess it, classify it using the pretrained ResNet-50 model, and visualize the image with its top prediction. It also prints the top five predictions with associated probabilities.

def classify_satellite_image(url):    response = requests.get(url)    img = Image.open(BytesIO(response.content)).convert('RGB')    input_tensor = preprocess(img)    input_batch = input_tensor.unsqueeze(0)    with torch.no_grad():        output = model(input_batch)    labels_url = "https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"    labels = requests.get(labels_url).text.split("n")    probabilities = torch.nn.functional.softmax(output[0], dim=0)    top5_prob, top5_catid = torch.topk(probabilities, 5)    plt.imshow(img)    plt.axis('off')    plt.title("Top Prediction: {}".format(labels[top5_catid[0]]))    plt.show()    print("Top 5 Predictions:")    for i in range(top5_prob.size(0)):        print(labels[top5_catid[i]], top5_prob[i].item())

Finally, we download a wildfire-related satellite image, classify it using the pretrained ResNet-50 model, and visually display it along with its top five predictions.

image_url = "https://upload.wikimedia.org/wikipedia/commons/0/05/Burnout_ops_on_Mangum_Fire_McCall_Smokejumpers.jpg"classify_satellite_image(image_url)

In conclusion, we’ve successfully harnessed IBM’s open-source ResNet-50 model in Google Colab to efficiently classify satellite imagery, supporting critical disaster assessment and response tasks. The approach outlined demonstrates the practicality and accessibility of advanced machine learning models and emphasizes how pretrained CNNs can be creatively applied to real-world challenges. With minimal setup, we now have a powerful tool at our disposal.

Here is the Colab Notebook. Also, don’t forget to follow us on Twitter and join our Telegram Channel and LinkedIn Group. Don’t Forget to join our 85k+ ML SubReddit.

The post Code Implementation of a Rapid Disaster Assessment Tool Using IBM’s Open-Source ResNet-50 Model appeared first on MarkTechPost.