This study explores object detection in historical aerial photographs of Namibia to identify long-term environmental changes. Specifically, we aim to identify key objects -- Waterholes, Omuti homesteads, and Big trees -- around Oshikango in Namibia using sub-meter gray-scale aerial imagery from 1943 and 1972. In this work, we propose a workflow for analyzing historical aerial imagery using a deep semantic segmentation model on sparse hand-labels. To this end, we employ a number of strategies including class-weighting, pseudo-labeling and empirical p-value-based filtering to balance skewed and sparse representations of objects in the ground truth data. Results demonstrate the benefits of these different training strategies resulting in an average $F_1=0.661$ and $F_1=0.755$ over the three objects of interest for the 1943 and 1972 imagery, respectively. We also identified that the average size of Waterhole and Big trees increased while the average size of Omuti homesteads decreased between 1943 and 1972 reflecting some of the local effects of the massive post-Second World War economic, agricultural, demographic, and environmental changes. This work also highlights the untapped potential of historical aerial photographs in understanding long-term environmental changes beyond Namibia (and Africa). With the lack of adequate satellite technology in the past, archival aerial photography offers a great alternative to uncover decades-long environmental changes.

Recent progress with conditional image diffusion models has been stunning, and this holds true whether we are speaking about models conditioned on a text description, a scene layout, or a sketch. Unconditional image diffusion models are also improving but lag behind, as do diffusion models which are conditioned on lower-dimensional features like class labels. We propose to close the gap between conditional and unconditional models using a two-stage sampling procedure. In the first stage we sample an embedding describing the semantic content of the image. In the second stage we sample the image conditioned on this embedding and then discard the embedding. Doing so lets us leverage the power of conditional diffusion models on the unconditional generation task, which we show improves FID by 25-50% compared to standard unconditional generation.

Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness. These problems remain difficult, especially when it is important to preserve semantic structures. Traditional image-level similarity metrics are of limited use, since the semantics of an image are high-level, and not strongly governed by pixel-wise faithfulness to an original image. Towards filling this gap, we introduce SAMScore, a generic semantic structural similarity metric for evaluating the faithfulness of image translation models. SAMScore is based on the recent high-performance Segment Anything Model (SAM), which can perform semantic similarity comparisons with standout accuracy. We applied SAMScore on 19 image translation tasks, and found that it is able to outperform all other competitive metrics on all of the tasks. We envision that SAMScore will prove to be a valuable tool that will help to drive the vibrant field of image translation, by allowing for more precise evaluations of new and evolving translation models. The code is available at https://github.com/Kent0n-Li/SAMScore.

Recent years, typhoon damages has become social problem owing to climate change. Especially, 9 September 2019, Typhoon Faxai passed on the south Chiba prefecture in Japan, whose damages included with electric and water provision stop and house roof break because of strong wind recorded on the maximum 45 meter per second. A large amount of tree fell down, and the neighbor electric poles also fell down at the same time. These disaster features have caused that it took eighteen days for recovery longer than past ones. Initial responses are important for faster recovery. As long as we can, aerial survey for global screening of devastated region would be required for decision support to respond where to recover ahead. This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography. This method can classify eight classes which contains land covers without damages and areas with disaster, where an aerial photograph is partitioned into 4,096 grids that is 64 by 64, with each unit image of 48 meter square. Using target feature class probabilities, we can visualize disaster features map to scale the color range from blue to red or yellow. Furthermore, we can realize disaster feature mapping on each unit grid images to compute the convolutional activation map using Grad-CAM based on deep neural network layers for classification. This paper demonstrates case studies applied to aerial photographs recorded at the south Chiba prefecture in Japan after typhoon disaster.

Here's what to do when a drone gets too close for comfort. Sam Sargent uses a DJI drone to get aerial photographs of a home for sale in the Crocker Highlands neighborhood of Oakland. Users here of the local social network Nextdoor for months have been stewing about these small, flying vehicles, which often carry cameras, accusing them of snooping or maybe casing the joint. They wonder if it's legal to fight back, say by lassoing the pesky vehicle flying outside their window – or even shooting it down with a potato gun. Oakland resident Katy O'Neill goes as far as blaming it for shattering her dining room window.

Recently, Tokyo terminated its traditional visual-identification work, which had been used for 20 years, and shifted to a new automated system. This article introduces the Fixed Assets Change Judgment (FACJ) system and its core tool, RealScape. RealScape automatically detects changes in the height and color of buildings based on three-dimensional analysis of aerial photographs. It employs a unique pixelby-pixel stereo processing method and enables a foot-level precision for each building. RealScape automatically detects changes in the height and color of buildings based on three-dimensional analysis of aerial photographs. The three-dimensional analysis employs a pixel-by-pixel stereo processing method that calculates the height of each pixel in aerial photographs and thus enables precise difference detection between previous and current aerial photographs. Since then, it has been used at its tax bureau every year to calculate the municipality's fixed-asset tax. After the success in Tokyo, other major city governments, including Osaka and Sapporo, have followed suit. The Japanese fixed-property tax is imposed by municipalities on the owners of land, buildings, and depreciation assets (all hereinafter referred to as "fixed assets") on January 1 of every year by calculating the tax sum according to current asset values.

CycleGAN (Zhu et al. 2017) is one recent successful approach to learn a transformation between two image distributions. In a series of experiments, we demonstrate an intriguing property of the model: CycleGAN learns to "hide" information about a source image into the images it generates in a nearly imperceptible, high-frequency signal. This trick ensures that the generator can recover the original sample and thus satisfy the cyclic consistency requirement, while the generated image remains realistic. We connect this phenomenon with adversarial attacks by viewing CycleGAN's training procedure as training a generator of adversarial examples and demonstrate that the cyclic consistency loss causes CycleGAN to be especially vulnerable to adversarial attacks.

The Tokyo Metropolitan Government, the largest municipality in Japan, routinely conducts building-change identification work. Recently, Tokyo terminated its traditional visual identification work, which had been used for 20 years, and shifted to a new automated system. This paper introduces the Fixed Assets Change Judgment (FACJ) system and its core tool, RealScape. RealScape detects building changes more accurately than visual judgment operations by humans and reduces the labor costs to one third of the traditional approach and the required judgment duration to about two weeks per 100 km2.

The Japanese fixed-property tax is imposed by municipalities on the owners of land, buildings, and depreciation assets (all hereinafter referred to as "fixed assets") on January 1 of every year by calculating the tax sum according to current asset values. This identification work is contracted out to survey companies. The identification of such en over a scale that can cover an actual area of 800 changes is entrusted to survey companies who hire by 600 meters or 500 by 600 meters (variable a large number of workers (figure 1, left). However, depending on the municipality), and every municipality reliance on human labor has led to problems has several hundred photographs that must detailed in the following paragraphs. Under these circumstances, the incentives for It takes about 10 hours to read and interpret a single the municipalities to overcome such challenges by photograph, and the average municipality automating or systematizing the photograph-reading must perform this work for several hundred photographs.

The Tokyo Metropolitan Government is the largest municipality in Japan and conducts building change identification work. Recently, Tokyo terminated its traditional visual identification work that has been in use for 20 years and shifted to a new automated system. This paper is intended to introduce the Fixed Assets Change Judgment (FACJ) system and its core tool, RealScape. RealScape automatically detects the changes in the height and color of buildings based on three-dimensional (3D) analysis of aerial photographs. It employs a unique pixel-by-pixel stereo processing method and enables the foot-level precision for each building. RealScape detects building changes more accurately than visual judgment operations by humans and reduces the labor costs to one third of the traditional approach and the required judgment duration to about two weeks per 100km2.