Automated Openstreetmap Data Alignment for Road Network Mapping

Semantic segmentation of remote sensing images enables in particular land-cover map generation for a given set of classes. Very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many tasks, from object recognition to semantic labelling, including the classification of Very High Resolution (VHR) satellite images. However, while plethora of works aim at improving object delineation on geographically restricted areas, few tend to solve this classification task at very large scales. New issues occur such as intra-class class variability, diachrony between surveys, and the appearance of new classes in a specific area, that do not exist in the predefined set of labels. Therefore, this work intends to (i) perform large scale classification and to (ii) expand a set of land-cover classes, using the off-the-shelf model learnt in a specific area of interest and adapting it to unseen areas.

Spatio-temporal analysis of Kilim River using Very High-Resolution (VHR) satellite image between the years of 2005 and 2012 are vital to assess river morphological changes and erosion detection over the specific time duration. Therefore, this study utilizes remote sensing and Geographical Information System (GIS) capabilities to identify the channel migration and shifting as well as to calculate the rate of erosion and accretion. Classification of the river from Quickbird and WorldView-2 images is carry out using Maximum Likelihood Classification (MLC) technique whereas feature selection tool performs to extract river feature from the raw image. Subsequently, river feature been overlaid to identify the changes of riverbank pattern and to calculate the rate of riverbank erosion in terms of area. The result shows the total area of erosion is 27252.959 m2 and the total area of accretion is 6079.999 m2. A maximum erosion of 6993.102 m2 detected at Section E while maximum accretion of 681.026 m2 spotted at Section D. The rates of riverbank erosion are 3406.62 m2/year while the rates for accretion are 759.999 m2/years between 2005 and 2012. Hence, the output of this study enables Langkawi Development Authority (LADA) and other stakeholders to recognize the specific location, which severely affected by erosion and accretion as well as to spot the area experienced huge river channel shifting.

Case instance segmentation of small farmland based on Mask R-CNN of feature pyramid network with double attention mechanism in high resolution satellite images

This paper presents a two-level Active Learning (AL) classification method for the interactive detection of earthquake-induced debris via the synergetic use of post-disaster Very High Resolution (VHR) satellite and local decimeter-resolution aerial images. The proposed method is performed by interactively guiding the human expert in the collection of labeled training samples from aerial images and optimally planning further aerial surveys. A label propagation mechanism is adopted to reliably propagate the labeled pixels annotated by the user on the aerial image by visual photointerpretation to the (lower resolution) satellite image. The experimental analysis is carried out using post-disaster images of the 2010 Yushu earthquake in China. The obtained results confirm that the proposed method can significantly reduce the user annotation effort and the cost for acquiring additional aerial images leading to more accurate and timely debris detection maps.

As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods.

China-Pakistan Economic Corridor (CPEC) is a framework of regional connectivity, which will not only benefit China and Pakistan but will have positive impact on Iran, Afghanistan, India, Central Asian Republic, and the region. The surrounding area in CPEC is prone to frequent disruption by geological hazards mainly landslides in northern Pakistan. Comprehensive landslide inventory and susceptibility assessment are rarely available to utilize for landslide mitigation strategies. This study aims to utilize the high-resolution satellite images to develop a comprehensive landslide inventory and subsequently develop landslide susceptibility maps using multiple techniques. The very high-resolution (VHR) satellite images are utilized to develop a landslide inventory using the visual image classification techniques, historic records and field observations. A total of 1632 landslides are mapped in the area. Four statistical models i.e., frequency ratio, artificial neural network, weights of evidence and logistic regression were used for landslide susceptibility modeling by comparing the landslide inventory with the topographic parameters, geological features, drainage and road network. The developed landslides susceptibility maps were verified using the area under curve (AUC) method. The prediction power of the model was assessed by the prediction rate curve. The success rate curves show 93%, 92.8%, 92.7% and 87.4% accuracy of susceptibility maps for frequency ratio, artificial neural network, weights of evidence and logistic regression, respectively. The developed landslide inventory and susceptibility maps can be used for land use planning and landslide mitigation strategies.

Urban Land Use/Land Cover (LULC) information is essential for urban and environmental management. It is, however, very difficult to automatically extract detailed urban LULC information from remote sensing imagery, especially for a large urban area. Medium resolution imagery, such as Landsat Thematic Mapper (TM) data, cannot uncover detailed LULC information. Further, very high resolution (VHR) satellite imagery, such as IKONOS and QuickBird data, can only be applied to a small area, largely due to the data unavailability and high computation cost. As a result, little research has been conducted to extract detailed urban LULC information for a large urban area. This study, therefore, developed a three-layer classification scheme for deriving detailedurban LULC information by integrating newly launched Chinese GF-1 (medium resolution) and GF-2 (very high resolution) satellite imagery and synthetically incorporating geometry, texture, and spectral information through multi-resolution image segmentation and object-based image classification (OBIA). Homogeneous urban LULC types such as water bodies or large areas of vegetation could be derived from GF-1 imagery with 16 m and 8 m spatial resolutions, while heterogeneous urban LULC types such as industrial buildings, residential buildings, and roads could be extracted from GF-2 imagery with 3.2 m and 0.8 m spatial resolutions. The multi-resolution segmentation method and a random forest algorithm were employed to perform image segmentation and object-based image classification, respectively. An analysis of the results suggests an overall accuracy of 0.89 and 0.87 were achieved for the second and third level urban LULC classification maps, respectively. Therefore, the three-layer classification scheme has the potential to derive high accuracy urban LULC information through integrating medium and high-resolution remote sensing imagery.

Satellite imagery has been used to improve scientific research worldwide. In this study, the southern right whale (Eubalaena australis) was chosen to test the use of medium, high, and very high resolution (VHR) satellite images, on the Brazilian breeding ground. These images were used to identify the whales and were compared to aerial survey data collected in the same area. The VHR satellite images from the Pleiades‐1A satellite, available on Google Earth, displayed the best results when compared to those from Sentinel 2, Landsat 8, Rapid Eye, and Planet Scope. No significant differences were observed (Mann‐Whitney U test) between the possible whales recorded in the satellite images and of real whales recorded in situ by aerial surveys, while considering either the number of groups (p = .841, n = 5) or the total number of animals (p = .222, n = 5). Further, when using VHR images, the geographical positions of the whales recorded in situ had a positive correlation with the positions generated from the satellite images (Mantel test: r = 0.52, p = .001, n = 13). This technique may represent an important tool for detecting right whales, especially in countries where research funding is scarce.

Oil palm trees detection and counting on Microsoft Bing Maps Very High Resolution (VHR) satellite imagery and Unmanned Aerial Vehicles (UAV) data using image processing thresholding approach

Third-dimension information is of a great importance for several remote sensing applications, such as building detection. The main data-source for these applications is very high resolution (VHR) satellite images which allow detailed mapping of complex environments. Stereo VHR satellite images allow the extraction of two correlated types of third-dimension information: disparity and elevation information. While the disparity is measured directly, the elevation information is derived computationally. To measure the disparity information, two overlapped images are matched. However, for the backward and forward off-nadir VHR stereo images, building facades occlude areas and hence create many data gaps. When the disparity is required to represent only the off-terrain objects, interpolation and normalization techniques are typically used. However, in dense urban environments, these techniques destroy the quality of the generated data. Therefore, this paper proposes a registration-based technique to measure the disparity of the above-ground objects. The technique includes constructing epipolar images and registering them using common terrain- level features to allow direct disparity mapping for the off-terrain objects. After the implementation, the negative effects of occlusion in the off-nadir VHR stereo images are mitigated through direct disparity mapping of the above-ground objects and bypassing the interpolation and normalization steps.

Reliable building delineation in very high resolution (VHR) satellite imagery can be achieved by precise disparity information extracted from stereo pairs. However, off-nadir VHR images over urban areas contain many occlusions due to building leaning that creates gaps in the extracted disparity maps. The typical approach to fill these gaps is by interpolation. However, it inevitably degrades the quality of the disparity map and reduces the accuracy of building detection. Thus, this research proposes a registration-based technique for mapping the disparity of off-terrain objects to avoid the need for disparity interpolation and normalization. The generated disparity by the proposed technique is then used to support building detection in off-nadir VHR satellite images. Experiments in a high-rise building area confirmed that 75 percent of the detected building roofs overlap precisely the reference data, with almost 100 percent correct detection. These accuracies are substantially higher than those achieved by other published research.

Open-source data-driven urban land-use mapping integrating point-line-polygon semantic objects: A case study of Chinese cities

Abstract. The Very High Resolution (VHR) satellite imageries such us Pleiades, WorldView-2, GeoEye-1 used for precise mapping purpose must be corrected from any distortion to achieve the expected accuracy. Orthorectification is performed to eliminate geometric errors of the VHR satellite imageries. Orthorectification requires main input data such as Digital Elevation Model (DEM) and Ground Control Point (GCP). The VHR systematic-ortho imageries were generated using SRTM 30m DEM without using any GCP data. The accuracy value differences of VHR systematic-ortho imageries and VHR orthorectified imageries using GCP currently is not exactly defined. This study aimed to identified the accuracy comparison of VHR systematic-ortho imageries against orthorectified imageries using GCP. Orthorectified imageries using GCP created by using Rigorous model. Accuracy evaluation is calculated by using several independent check points.

The number of high and very high resolution (VHR) optical satellite sensors, as well as the number of medium resolution satellites is continuously growing. However, not all high-resolution optical satellite imaging cameras have a sufficient and stable calibration in time. Due to their high agility in rotation, a quick change in viewing direction can lead to satellite attitude oscillation, causing image distortions and thus affecting image geometry and geo-positioning accuracy. This paper presents an approach based on re-projection of regularly distributed 3D ground points from object in image space, to detect and estimate the periodic distortions of Pléiades tri-stereo imagery caused by satellite attitude oscillations. For this, a hilly region was selected as a test site. Consequently, we describe a complete processing pipeline for computing the systematic height errors (deformations, waves) of the satellite-based digital elevation model by using a Lidar high resolution terrain model. Ground points with fixed positions, but with two elevations (actual and corrected) are then re-projected to the satellite images with the aid of the Rational Polynomial Coefficients (RPCs) provided with the imagery. Therefore, image corrections (displacements) are determined by computing the differences between the distinct positions of corresponding points in image space. Our experimental results in Allentsteig (Lower Austria) show that the systematic height errors of satellite-based elevation models cannot be compensated with an usual or even high number of Ground Control Points (GCPs) for RPC bias correction, due to insufficiently known image orientations. In comparison to a reference Lidar Digital Terrain Model (DTM), the computed elevation models show undulation effects with a maximum height difference of 0.88 m in along-track direction. With the proposed method, image distortions in-track direction with amplitudes of less than 0.15 pixels were detected. After applying the periodic distortion compensation to all three images, the systematic elevation discrepancies from the derived elevation models were successfully removed and the overall accuracy in open areas improved by 33% in the RMSE. Additionally, we show that a coarser resolution reference elevation model (AW3D30) is not feasible for improving the geometry of the Pléiades tri-stereo satellite imagery.

Geo-information extraction from satellite imagery has become crucial to carry out large-scale ground surveys in a short amount of time. With the increasing number of commercial satellites launched into orbit in recent years, high-resolution RGB color remote sensing imagery has attracted a lot of attention. However, because of the high cost of image acquisition and even more complicated annotation procedures, there are limited high-resolution satellite datasets available. Compared to close-range imagery datasets, existing satellite datasets have a much lower number of images and cover only a few scenarios (cities, background environments, etc.). They may not be sufficient for training robust learning models that fit all environmental conditions or be representative enough for training regional models that optimize for local scenarios. Instead of collecting and annotating more data, using synthetic images could be another solution to boost the performance of a model. This article proposes a GAN-assisted training scheme for road segmentation from high-resolution RGB color satellite images, which includes three critical components: (a) synthetic training sample generation, (b) synthetic training sample selection, and (c) assisted training strategy. Apart from the GeoPalette and cSinGAN image generators introduced in our prior work, this article explains in detail how to generate new training pairs using OpenStreetMap (OSM) and introduces a new set of evaluation metrics for selecting synthetic training pairs from a pool of generated samples. We conduct extensive quantitative and qualitative experiments to compare different image generators and training strategies. Our experiments on the downstream road segmentation task show that (1) our proposed metrics are more aligned with the trained model performance compared to commonly used GAN evaluation metrics such as the Fréchet inception distance (FID); and (2) by using synthetic data with the best training strategy, the model performance, mean Intersection over Union (mean IoU), is improved from 60.92% to 64.44%, when 1,000 real training pairs are available for learning, which reaches a similar level of performance as a model that is standard-trained with 4,000 real images (64.59%), i.e., enabling a 4-fold reduction in real dataset size.