The Planet PlanetScope cubesat constellation acquires high-resolution optical images which cover the entire surface of the Earth daily, enabling an unprecedented capability to monitor the Earth surface changes. However, our analysis reveals artifacts of the geometry of PlanetScope images related to the imaging system and processing issues, limiting the usability of these data for various earth science applications, including the monitoring of glaciers, dune motion, or the measurement of ground deformation due to earthquakes and landslides. Here we analyze these artifacts and propose ways to remediate them. We use two examples to evaluate the data and assess the performance of our proposed approaches. The first is the ground deformation caused by the 2019 Ridgecrest earthquake sequence, California, and the second is the 2018-2019 surge of the Shisper glacier in the Karakorum. Using an image correlation technique, we show that PlanetScope images exhibit several geometric artifacts, such as scene-to-scene misregistration, inconsistence geolocation accuracy between spectral bands, and topographic artifacts. Altogether these artifacts make a quantitative analysis of ground displacement difficult and inaccurate. We present a method that remediates most of these geometric artifacts. In addition, we propose a framework for selecting the most appropriate images and a procedure for refining the Rational Function Model (RFM) of unrectified images to monitor surface displacements and topography changes in 3D. These tools should enhance the use of PlanetScope images for Earth Science applications.

This paper presents a new methodology for the calculation of a digital elevation model (DEM) and the corresponding orthophotography at very high resolution (20 cm for the DEM, 10 cm for the orthophotography) on a large surface (more than 3 km2). Image acquisition was done with an off-the-shelf camera (Sony NEX-5N) attached to a delta kite. Developing the most accessible methodology being one of the goals of this study, the overall cost of the apparatus is limited to less than 1500 euros. This method has been developed to form an alternative with acquisitions from small Unmanned Aerial Vehicles (UAV). In particular, kite as payload carrier is seen as being complementary to lightweight multicopters, which are increasingly used for remote sensing applications [1, 2]. Kite is of a strong interest when the use of radio-controlled and motorized systems is hampered by local regulation and/or when too strong winds keep these lightweight devices on ground.

In the past few years, many change detection methods have been proposed in the GIS community to support the update of existing building databases. If most of these methods use images to settle the problem, only a few of them analyse the optimal resolution to use for input images. The main goal of this paper - that focuses on the use of satellite images - is to provide possible answers to this question. For that purpose, a change detection method is tested on several test areas, for which input images are available at different resolutions (50 cm and 70 cm). The outcomes of the method are then evaluated and analysed in order to highlight the impact of the spatial resolution of input images on the system's efficiency.

We present a new approach for building reconstruction from a single Digital Surface Model (DSM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGO set). First, the 2D-supports of the urban structures are extracted either interactively or automatically. Then, 3D-blocks are placed on the 2D-supports using a Gibbs model which controls both the block assemblage and the fitting to data. A Bayesian decision finds the optimal configuration of 3D-blocks using a Markov Chain Monte Carlo sampler associated with original proposition kernels. This method has been validated on multiple data set in a wide-resolution interval such as 0.7 m satellite and 0.1 m aerial DSMs, and provides 3D representations on complex buildings and dense urban areas with various levels of detail.

In this paper, we present an automatic method for deriving Digital Terrain Models (DTM) from Digital Surface Models (DSM). The DTM generation is formalized here as the minimization of a given energy, defined by a data term and a regularization term. The main contribution of this paper lies in the specific module, introduced in the data term in order to filter outliers (non ground points present in the original DSM) so that the final terrain surface best fits true ground points (inliers). The method is here introduced and tested on a vegetated and hilly area. A comparison to ground truth is also performed and shows the geometric accuracy and the robustness of the approach.

Full-waveform (FW) lidar systems provide range profiles of the Earth topography. They are acquired from airborne platforms or from satellites. Many applications derive from the use of such data, from the extraction of 3D point clouds to the inversion of vegetation profiles. Nevertheless, handling range profiles is much more difficult than handling 3D point cloud. The aim of this paper is to present a research tool based on opensource libraries that can process and visualize such data. We focused our work on the implementation on the 2D/3D interface that gives the possibility to visualize the interaction between the lidar electromagnetic waves and the Earth topography. Moreover, this tool integrates several processing steps of FW Lidar data.

We present a new approach for building reconstruction from a single Digital Elevation Model (DEM). It treats buildings as an assemblage of simple urban structures extracted from a library of 3D parametric blocks (like a LEGOregset). This method works on various data resolutions such as 0.7 m satellite and 0.1 m aerial DEMs and allows us to obtain 3D representations with various levels of detail. First, the 2D supports of the urban structures are extracted either interactively or automatically. Then, 3D blocks are placed on the 2D supports using a Gibbs model. A Bayesian decision finds the optimal configuration of 3D blocks using a RJMCMC sampler. Experimental results on complex buildings and dense urban areas are presented using data at various resolutions.

This paper describes an automatic method for the detection and detailed reconstruction of 3D building models that include roof superstructures such as dormer windows or chimneys from a very high resolution digital elevation model. Buildings are reconstructed as a set of roof planes with a set of parametric shapes that model roof superstructures. The proposed model-based approach minimizes a minimum description length energy.

In this paper, we present an automatic method for the 3D building reconstruction from satellite images. The proposed approach consists in reconstructing buildings by assembling simple urban structures extracted from a grammar of 3D parametric models, as a "LEGO" game. First, the building footprints are extracted through sequences of quadrilaterals: it allows to define the problem as a causal process. Then, the 3D reconstruction stage is realized through a Hidden Markov Model and the optimal sequences of 3D parametric objects are found using the Viterbi algorithm.

We propose two algorithms to match edges in a geometrically-imprecise graph to geometrically-precise strong boundaries in an image, where the graph is meant to give an a priori partition of the image into objects. This can be used to partition an image into objects described by imprecise external data, and thus to simplify the segmentation problem. We apply them to the problem of registering cadastre data to georeferenced aerial images, thus correcting the lack of geometrical detail of the cadastre data, and the fact that cadastre data gives information of a different nature than that found in images (fiscal information as opposed to actual land use).

We propose a parametric model for automatic 3D reconstruction of urban areas from high resolution satellite data. An automatic building extraction method based on marked point processes is used to provide rectangular building footprints. Based on a parametric model with rectangular ground footprint, the proposed method is developed using a Bayesian approach : we search for the best configuration of parametric models with respect to both a priori knowledge of models and their interactions, and a likelihood which fits models to the DEM. A simulated annealing is used to find the configuration which maximizes the a posteriori density of the Bayesian expression

We present an automatic 3D city model of dense urban areas from HR satellite data. The proposed method is developed using a structural approach: we construct complex buildings by merging simple parametric models with rectangular ground footprint. To do so, an automatic building extraction method based on marked point processes is used to provide rectangular building footprints. A collection of 3D parametric models is defined in order to be fixed onto these building footprints. A Bayesian framework including both prior knowledge of models and their interactions, and a likelihood fitting them to the digital elevation model, is then used. A simulated annealing scheme allows to find the configuration which maximizes the posterior density of the Bayesian expression.

This research work is a part of a global project related to urban scene modeling from high resolution satellite images with focus on building production. The input data consist of a panchromatic stereo pair of satellite images, with a submetric resolution of 45-70 cm and a low base to height ratio B/H [0.05 - 0.2]. Data are provided by CNES. Since a detailed extraction and description of building rooves is complex in a satellital context, we propose to describe the scene by means of a 3D-surface which provides either raster or vector information using different level description. From extracted primitives and raw correlation information, an hybrid 3D surface will be obtained by an energy minimization process via graph cuts. The main contribution of our approach is the use of 3D-primitives such as 3D-segments and 3D-facets as well as the introduction of information from external database to guide the optimization process. The obtained product is an hybrid DEM (digital elevation model) which provides the highest level of reliable primitives for each scene region.

This paper proposes a contribution to the adjustment problem of airborne laser scanner strips. Based the comparison of each laser strips with a photogrammetric derived Digital Surface Model (DSM), and on the modeling of the discrepancies between these two data sets, the algorithm produces an homogeneous 3D deformation field. The measurements of these deformations is performed using a modified Hough transform and we search for the maximum of the accumulator whereupon we assign a probability of correctness. Depending of this probability, we estimate a global affine transform over the cloud of points. For transitivity properties, all strips are registered with regard to this DSM and therefore they all perfectly fit on overlapping areas. This fine registration is of importance for many applications, especially when fusionning photogrammetric data and lidar data for feature extraction. More generally, this method can be applied to any other sorts of 3D data and provides an efficient algorithm for matching 3D datasets

This paper deals with cadastral maps interpretation device. The challenge is to propose a complete reconstruction of the parcel's areas and buildings to use with geographic information systems. It is based on a low level primitives extraction and classification. As this low level may be quite noisy, an interpretation process classifies medium level objects and manages convenient processes to the particular extracted shape. Then, a reconstruction step is used to label the parcels areas and determine the final land partition. We present at first the vectorization strategy in our particular context then we will discuss the different tools used to reach the higher level.

This paper deals with the automatic quotation of the contour lines extracted from a scanned paper map, in the way to create a DTM (digital terrain model). The great number of contour line interruptions, the connections between two (or more) contour lines and the low density of the elevation information contained on the map, make the quotation task very difficult. Our technique is based on the computation of an intermediate DTM from the global analysis of the main contour lines and the elevation dots. By using this intermediate DTM, we are able to estimate the contour line orientation (i.e. the slopeway), and then to quote the contour lines. By analyzing the trust on the orientation and the coherence of the quotation transitions, we select all the unsuspicious elevations and use them to create a new DTM. We assume that this new DTM is more precise than the intermediate DTM. So we can use, if necessary, this new DTM as a new intermediate DTM to repeat the process (orientation estimation and quotation).

A project on topographic maps interpretation is being driven at I.G.N. (Institut Geographique National, France). The aim of this project is to provide a totally automatic interpretation with a call to operator on the only suspicious cases detected by the program. This paper presents an automatic system for the creation of a Digital Terrain Model (DTM) out of a scanned map. Our proposed method is based on the use of external elevation information and on a combined local/global strategy for quoting the portions of curve present on the map. To reduce the combinatory of the problem, and to accelerate the convergence, all the "trivial cases" of interruption are pretreated. After having quoted the portions of curve, a DTM is created by a classical interpolation method.

We introduce the new concept of "veinerization", which produces a graph that contains all the "topological" information needed to derive a wide variety of skeletons. Theoretically, the main contribution is to provide a homogeneous framework for integration of the major concepts described in other related works on digital skeletonization. In practice, the new aspect of this approach is to provide the user with different criteria for selecting the most suitable skeleton for a given application, e.g., the user can select a suitable threshold for obtaining the desirable balance between " having a skeleton without noisy prunes" and "having a skeleton that reflects the initial shape". This algorithm has been tested on numerous kinds of patterns, including pathological ones like fractal sets well-known for the complexity of their shapes.

We present a complete method for character string extraction that is specifically adapted to a cartographic context (orientation,...). We emphasize a high level reconstruction process that: (1) resolves the ambiguities remaining from pattern analysis; (2) structures the characters into strings. The knowledge used by this process is the coherence of strings to be constructed (e.g. in orientation, scale, fonts...). We formalize the problem in the most general way and then admit two simplifying hypotheses that transform the problem into a graph optimization problem. The method implemented for solving this problem uses different techniques such as: graph theory, dynamic programming, heuristic, combinatorial exploration.... The results seem sufficiently good for an industrial application.

The authors describe a high level method for reconstruction of a road network graph extracted from scanned maps. The corrupted graph that results from low-level treatments contains all the information needed for a correct reconstruction if one takes into account the a priori knowledge. A list of rules that contains this knowledge, is given. Formally, the problem is to construct the most likely graph considering a priori knowledge and the corrupted graph with which one started. An algorithmic approach is presented. The authors describe the different algorithmic steps used; each step uses a specific subset of the rules considered. As much as possible, to proceed by global optimization inside each step. The results obtained are good enough for use in GISs (geographic information systems) except for cities.>