Detection of Runway and Obstacles using Electro-optical and Infrared Sensors before Landing

For safe aircraft operations, detection of runway incursions especially during landing and takeoff is essential. And it is important that such detection technique is capable of detecting the distant objects so that pilot has enough response time to take corrective action. This paper presents techniques to detect runway and runway incursions using electro-optical color camera and medium wave infrared sensor on-board the aircraft during approach for landing. The detection process consists of horizon detection to reduce runway search space in sensor image and then detect runway and obstacles. The information is then presented to the pilot to improve pilot situational awareness. The performance of the proposed techniques are evaluated in flight simulators with simulated images of electro-optical and infrared sensors on-board the aircraft during approach for landing at a distance of 3 nautical miles from runway threshold during day/night and in low visibility CAT II foggy conditions. Effectiveness of the techniques with statistics of runway detection, miss detection and false alarm for different case studies have been provided and discussed.Defence Science Journal, Vol. 64, No. 1, January 2014, DOI:10.14429/dsj.64.276

Real-time Accurate Runway Detection based on Airborne Multi-sensors Fusion

Existing methods of runway detection are more focused on image processing for remote sensing images based on computer vision techniques. However, these algorithms are too complicated and time-consuming to meet the demand for real-time airborne application. This paper proposes a novel runway detection method based on airborne multi-sensors data fusion which works in a coarse-to-fine hierarchical architecture. At the coarse layer, a vision projection model from world coordinate system to image coordinate system is built by fusing airborne navigation data and forward-looking sensing images, then a runway region of interest (ROI) is extracted from a whole image by the model. Furthermore, EDLines which is a real-time line segments detector is applied to extract straight line segments from ROI at the fine layer, and fragmented line segments generated by EDLines are linked into two long runway lines. Finally, some unique runway features (e.g. vanishing point and runway direction) are used to recognise airport runway. The proposed method is tested on an image dataset provided by a flight simulation system. The experimental results show that the method has advantages in terms of speed, recognition rate and false alarm rate

LARD -- Landing Approach Runway Detection -- Dataset for Vision Based Landing

As the interest in autonomous systems continues to grow, one of the major challenges is collecting sufficient and representative real-world data. Despite the strong practical and commercial interest in autonomous landing systems in the aerospace field, there is a lack of open-source datasets of aerial images. To address this issue, we present a dataset-lard-of high-quality aerial images for the task of runway detection during approach and landing phases. Most of the dataset is composed of synthetic images but we also provide manually labelled images from real landing footages, to extend the detection task to a more realistic setting. In addition, we offer the generator which can produce such synthetic front-view images and enables automatic annotation of the runway corners through geometric transformations. This dataset paves the way for further research such as the analysis of dataset quality or the development of models to cope with the detection tasks. Find data, code and more up-to-date information at https://github.com/deel-ai/LAR

A DEEP LEARNING APPROACH FOR AIRPORT RUNWAY IDENTIFICATION FROM SATELLITE IMAGERY

The United States lacks a comprehensive national database of private Prior Permission Required (PPR) airports. The primary reason such a database does not exist is that there are no federal regulatory obligations for these facilities to have their information re-evaluated or updated by the Federal Aviation Administration (FAA) or the local state Department of Transportation (DOT) once the data has been entered into the system. The often outdated and incorrect information about landing sites presents a serious risk factor in aviation safety. In this thesis, we present a machine learning approach for detecting airport landing sites from Google Earth satellite imagery. The approach presented in this thesis plays a crucial role in confirming the FAA\u27s current database and improving aviation safety in the United States. Specifically, we designed, implemented, and evaluated object detection and segmentation techniques for identifying and segmenting the regions of interest in image data. The in-house dataset has been thoroughly annotated that includes 400 satellite images with a total of 700 instances of runways. The images - acquired via Google Maps static API - are 3000x3000 pixels in size. The models were trained using two distinct backbones on a Mask R-CNN architecture: ResNet101, and ResneXt101, and obtained the highest average precision score @0.75 with ResNet-101 at 92% and recall at 89%. We finally hosted the model in the StreamLit front-end platform, allowing users to enter any location to check and confirm the presence of a runway

Fusion of enhanced and synthetic vision system images for runway and horizon detection

Networked operation of unmanned air vehicles (UAVs) demands fusion of information from disparate sources for accurate flight control. In this investigation, a novel sensor fusion architecture for detecting aircraft runway and horizons as well as enhancing the awareness of surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented in real-world situations due to signal misalignment. We address this through a registration step to align EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented, and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. The fusion architecture developed in this study holds promise for incorporation into manned heads-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provides a basis for rule selection in other signal fusion applications

Fast automatic airport detection in remote sensing images using convolutional neural networks

Fast and automatic detection of airports from remote sensing images is useful for many military and civilian applications. In this paper, a fast automatic detection method is proposed to detect airports from remote sensing images based on convolutional neural networks using the Faster R-CNN algorithm. This method first applies a convolutional neural network to generate candidate airport regions. Based on the features extracted from these proposals, it then uses another convolutional neural network to perform airport detection. By taking the typical elongated linear geometric shape of airports into consideration, some specific improvements to the method are proposed. These approaches successfully improve the quality of positive samples and achieve a better accuracy in the final detection results. Experimental results on an airport dataset, Landsat 8 images, and a Gaofen-1 satellite scene demonstrate the effectiveness and efficiency of the proposed method

Automatic Update of Airport GIS by Remote Sensing Image Analysis

This project investigates ways to automatically update Geographic Information Systems (GIS) for airports by analysis of Very High Resolution (VHR) remote sensing images. These GIS databases map the physical layout of an airport by representing a broad range of features (such as runways, taxiways and roads) as georeferenced vector objects. Updating such systems therefore involves both automatic detection of relevant objects from remotely sensed images, and comparison of these objects between bi-temporal images. The size of the VHR images and the diversity of the object types to be captured in the GIS databases makes this a very large and complex problem. Therefore we must split it into smaller parts which can be framed as instances of image processing problems. The aim of this project is to apply a range of methodologies to these problems and compare their results, providing quantitative data where possible. In this report, we devote a chapter to each sub-problem that was focussed on. Chapter 1 begins by introducing the background and motivation of the project, and describes the problem in more detail. Chapter 2 presents a method for detecting and segmenting runways, by detecting their distinctive markings and feeding them into a modified Hough transform. The algorithm was tested on a dataset of six bi-temporal remote sensing image pairs and validated against manually generated ground-truth GIS data, provided by Jeppesen. Chapter 3 investigates co-registration of bi-temporal images, as a necessary precursor to most direct change detection algorithms. Chapter 4 then tests a range of bi-temporal change detection algorithms (some standard, some novel) on co-registered images of airports, with the aim of producing a change heat-map which may assist a human operator in rapidly focussing attention on areas that have changed significantly. Chapter 5 explores a number of approaches to detecting curvilinear AMDB features such as taxilines and stopbars, by means of enhancing such features and suppressing others, prior to thresholding. Finally in Chapter 6 we develop a method for distinguishing between AMDB lines and other curvilinear structures that may occur in an image, by analysing the connectivity between such features and the runways

GLOBAL BARE GROUND GAIN BETWEEN 2000 AND 2012 AND THE RELATIONSHIP WITH SOCIOECONOMIC DEVELOPMENT

Bare ground gain -- the complete removal of vegetation due to land use changes, represents an extreme land cover transition that completely alters the structure and functioning of ecosystems. The fast expansion of bare ground cover is directly associated with increasing population and urbanization, resulting in accelerated greenhouse gas emissions, intensified urban heat island phenomenon, and extensive habitat fragments and loss. While the economic return of settlement and infrastructure construction has improved human livelihoods, the negative impacts on the environment have disproportionally affected vulnerable population, creating inequality and tension in society. The area, distribution, drivers, and change rates of global bare ground gain were not systematically quantified; neither was the relationship between such dynamics and socioeconomic development. This dissertation seeks methods for operational characterization of bare ground expansion, advances our understanding of the magnitudes, dynamics, and drivers of global bare ground gain between 2000 and 2012, and uncovers the implications of such change for macro-economic development monitoring, all through Landsat satellite observations. The approach that employs wall-to-wall maps of bare ground gain classified from Landsat imagery for probability sample selection is proved particularly effective for unbiased area estimation of global, continental, and national bare ground gain, as a small land cover and land use change theme. Anthropogenic land uses accounted for 95% of the global bare ground gain, largely consisting of commercial/residential built-up, infrastructure development, and resource extraction. China and the United States topped the total area increase in bare ground. Annual change rates of anthropogenic bare ground gain are found as a leading indicator of macro-economic change in the study period dominated by the 2007-2008 global financial crisis, through econometric analysis between annual gains in the bare ground of different land use outcomes and economic fluctuations in business cycles measured by detrended economic variables. Instead of intensive manual interpretation of land-use attributes of probability sample, an approach of integrating a pixel- and an object- based deep learning algorithms is proposed and tested feasible for automatic attribution of airports, a transportation land use with economic importance