Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning

• URL: http://arxiv.org/abs/2104.10874v1
• Date: Thu, 22 Apr 2021 05:31:13 GMT
• Title: Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning
• Authors: Savvas Karatsiolis and Andreas Kamilaris
• Abstract summary: We propose a task-focused Deep Learning model that takes advantage of the shadow map of a remotely sensed image to calculate its heightmap. We validate the model with a dataset covering a large area of Manchester, UK.
• Score: 3.42658286826597
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Estimating the heightmaps of buildings and vegetation in single remotely sensed images is a challenging problem. Effective solutions to this problem can comprise the stepping stone for solving complex and demanding problems that require 3D information of aerial imagery in the remote sensing discipline, which might be expensive or not feasible to require. We propose a task-focused Deep Learning (DL) model that takes advantage of the shadow map of a remotely sensed image to calculate its heightmap. The shadow is computed efficiently and does not add significant computation complexity. The model is trained with aerial images and their Lidar measurements, achieving superior performance on the task. We validate the model with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest Lidar dataset. Our work suggests that the proposed DL architecture and the technique of injecting shadows information into the model are valuable for improving the heightmap estimation task for single remotely sensed imagery.

Related papers

• An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset [2.048226951354646]
  We present a method for generating ground-truth disparity maps directly from Light Detection and Ranging (LiDAR) and images. We evaluate 11 dense matching methods across datasets with diverse scene types, image resolutions, and geometric configurations.
  arXiv  Detail & Related papers  (2024-02-19T20:33:46Z)
• Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data [87.61900472933523]
  This work presents Depth Anything, a highly practical solution for robust monocular depth estimation. We scale up the dataset by designing a data engine to collect and automatically annotate large-scale unlabeled data. We evaluate its zero-shot capabilities extensively, including six public datasets and randomly captured photos.
  arXiv  Detail & Related papers  (2024-01-19T18:59:52Z)
• Simple and Effective Synthesis of Indoor 3D Scenes [78.95697556834536]
  We study the problem of immersive 3D indoor scenes from one or more images. Our aim is to generate high-resolution images and videos from novel viewpoints. We propose an image-to-image GAN that maps directly from reprojections of incomplete point clouds to full high-resolution RGB-D images.
  arXiv  Detail & Related papers  (2022-04-06T17:54:46Z)
• Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks [21.788338971571736]
  Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications. This paper addresses the issue of building height retrieval in large-scale urban areas from a single TerraSAR-X spotlight or stripmap image.
  arXiv  Detail & Related papers  (2021-11-18T00:39:48Z)
• Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth [64.29043589521308]
  We propose a rendering module to augment the training data by synthesizing images with virtual-depths. The rendering module takes as input the RGB image and its corresponding sparse depth image, outputs a variety of photo-realistic synthetic images. Besides, we introduce an auxiliary module to improve the detection model by jointly optimizing it through a depth estimation task.
  arXiv  Detail & Related papers  (2021-07-28T11:00:47Z)
• Salient Objects in Clutter [130.63976772770368]
  This paper identifies and addresses a serious design bias of existing salient object detection (SOD) datasets. This design bias has led to a saturation in performance for state-of-the-art SOD models when evaluated on existing datasets. We propose a new high-quality dataset and update the previous saliency benchmark.
  arXiv  Detail & Related papers  (2021-05-07T03:49:26Z)
• Towards General Purpose Geometry-Preserving Single-View Depth Estimation [1.9573380763700712]
  Single-view depth estimation (SVDE) plays a crucial role in scene understanding for AR applications, 3D modeling, and robotics. Recent works have shown that a successful solution strongly relies on the diversity and volume of training data. Our work shows that a model trained on this data along with conventional datasets can gain accuracy while predicting correct scene geometry.
  arXiv  Detail & Related papers  (2020-09-25T20:06:13Z)
• Counting from Sky: A Large-scale Dataset for Remote Sensing Object Counting and A Benchmark Method [52.182698295053264]
  We are interested in counting dense objects from remote sensing images. Compared with object counting in a natural scene, this task is challenging in the following factors: large scale variation, complex cluttered background, and orientation arbitrariness. To address these issues, we first construct a large-scale object counting dataset with remote sensing images, which contains four important geographic objects. We then benchmark the dataset by designing a novel neural network that can generate a density map of an input image.
  arXiv  Detail & Related papers  (2020-08-28T03:47:49Z)
• Height estimation from single aerial images using a deep ordinal regression network [12.991266182762597]
  We deal with the ambiguous and unsolved problem of height estimation from a single aerial image. Driven by the success of deep learning, especially deep convolution neural networks (CNNs), some researches have proposed to estimate height information from a single aerial image. In this paper, we proposed to divide height values into spacing-increasing intervals and transform the regression problem into an ordinal regression problem.
  arXiv  Detail & Related papers  (2020-06-04T12:03:51Z)
• Counting dense objects in remote sensing images [52.182698295053264]
  Estimating number of interested objects from a given image is a challenging yet important task. In this paper, we are interested in counting dense objects from remote sensing images. To address these issues, we first construct a large-scale object counting dataset based on remote sensing images. We then benchmark the dataset by designing a novel neural network which can generate density map of an input image.
  arXiv  Detail & Related papers  (2020-02-14T09:13:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.