# A Neural-Network-Based Landscape Search Engine:
### Background  
Implementations of DL in remote sensing and within the broader field of geographic information science (GIScience) have been applied to a variety of tasks, such as land cover mapping [2], environmental parameter retrieval [3], data fusion and downscaling [4], object detection [5], and information construction and prediction (see [1; 6; 7] for comprehensive overviews). Other efforts have focused on advancing the principles of DL in remote sensing, including the integration of aerial images, and the detection of small objects on the landscape [5; 8]. [PERSON] et al. [3], in particular, have advocated for the fusion of geographic principles into DL for remote sensing tasks, most notably [PERSON]'s famous First Law of Geography: \"Everything is related to everything else, but near things are more related than distant things\". The most common and mainstream frameworks are back-propagation NNs, such as convolutional neural networks (CNNs). Indicative of their power, CNN models often produce a sizable increase in accuracy over traditional regression models, particularly when working with remotely sensed data. Further, unlike traditional learning algorithms, intrinsic features from raw input data can be extracted using a variety of DL frameworks without using manual digitizing techniques, thus reducing the need for reliance on domain knowledge [9]. Despite the significant number of remote sensing studies which utilize DL, there is a paucity of research on the particular task of image retrieval using remotely sensed data, with a few notable exceptions. [PERSON] et al. [10] first coined the term \"landscape search engine\" in building a landscape similarity tool for terrain across the entire country of Poland. Using the concept of \"geomorphons\", this approach classifies pixels from digital elevation models (DEMs) into several types: ridge, shoulder, spur, slope, hollow, footslope, valley, pit, flat, and peak. Another landscape similarity tool, developed by [PERSON] et al. [11], has been applied toward location optimization of control sites based on the spatial characteristics of treatment sites. The researchers applied a moving window analysis to generate per-pixel maps of similarity between the treatment and control areas for site selections. Outside of this application, the United States Geological Survey (USGS) has a landscape search tool focusing on land treatment exploration within the United States, making use of modifiable parameters, such as soil and vegetation characteristics [12]. Through an interactive web map, it allows users to input empirical characteristics for the purpose of finding areas with similar heat load, soil properties, and climate conditions. At the time of writing, however, the two formerly mentioned studies do not have publicly available toolkits, and none of these prior implementations make use of NNs. VGG16, ResNet-50, and the Neural Architecture Search Network (NasNet) have been used frequently in remote sensing. The Visual Geometry Group (VGG) model architecture is a standard CNN which uses a specified number of consecutive convolutional layers to extract features from image data. The input of VGG is an image with resolution 224 \(\times\) 224, and, since VGGNet is a classification network, the output shape is proportional to the number of classes in the dataset. The model architecture consists of multiple convolution layers followed by max pooling layers, and the end of the model consists of fully connected layers followed by the final classification layer. Two common VGG architectures used are VGG16 and VGG19, which are sixteen and nineteen layers deep, respectively [13]. The VGG16 architecture, in particular, was first introduced by [PERSON] and [PERSON] [14] for image recognition and has been used extensively in multispectral and hyperspectral image classifications even with low resolution imagery [15]. It has also been utilized for tasks such as road feature extraction [16], sea ice classification [17], image stitching [18], and many others.