Clean Map Research Articles

Multi-task deep learning for quantifying methane emissions from 2-D plume imagery with Low Signal-to-Noise Ratio

ABSTRACT Methane is the second most significant greenhouse gas after carbon dioxide. As global warming intensifies, the quantification of methane point sources is becoming increasingly crucial. However, retrieving high-quality methane signals from remote sensing data remains challenging due to various factors, including surface reflectance, atmospheric interference, sensor noise, wind speed, and sensor sensitivity. In real-world scenarios, methane remote sensing quantification often encounters unfavourable conditions that lead to low signal-to-noise ratio (SNR) signals, resulting in reduced quantification accuracy. To address these challenges, we introduce a novel multi-task learning-based approach. Specifically, we incorporate a denoising auxiliary task into the quantification network by introducing an additional denoising branch that recovers clean plume column concentration maps. The network is trained with supervision using both denoising and quantification losses, which enables it to acquire robust feature representations to noise and benefits the quantification of low SNR images. During the inference phase, the denoising branch is removed, resulting in an efficient and robust single quantification network with reduced inferring time, supporting onboard computation. We construct a low SNR database based on the AVIRIS-NG sensor and evaluate the generalization ability of our method. DQNet achieves the highest RMSE, MAPE, and R of 16.478 kg/h, 15.296%, and 95.167% respectively on the synthetic dataset. Across the entire range of SNR, DQNet exhibits a greater relative advantage as SNR decreases. However, our approach is not limited to AVIRIS-NG and can be easily extended to various multispectral and hyperspectral satellites.

Evaluation and development of the clean water distribution network in Pancang Village, North Sebatik District, Nunukan Regency

Background: Rapid population growth and the large number of residential developments go hand in hand with the increasing need for clean water, especially drinking water. Basically, a city's development plan is closely related to the availability of clean water in the area, because clean water is a basic human need that is very important to fulfill. However, several consumers in certain villages which are part of the PDAM North Sebatik branch pipe network service area do not receive a clean water supply. With this research, it is hoped that it can provide alternatives/solutions in solving clean water problems, especially for the Pancang Village area, North Sebatik District. Methods: The data needed in this study is data on the population of Sei Pancang Village, North Sebatik District for the last 5 years, data on PDAM customers for the last 5 years, data on clean water needs, data on clean water sources and reservoir volume, as well as map data on the clean water distribution network. The evaluation technique used in this study uses the method with the largest correlation calculation, namely the arithmetic method. The clean water distribution network map will be evaluated using epanet software. Findings: The results of this research are that the population of Sei Pancang Village, North Sebatik District in 2022 will be 5250 people and projections for the number of residents and customers for the next 10 years show that the population will increase to 7018 people. Conclusion: Water use in the development plan year, namely 2023, in the residential area of Sei Pancang Village, North Sebatik District, includes, among other things, an average daily need of 9,045 lt/s, a maximum daily need of 10,402 lt/s, and a peak hour need of 13,568 lt/s. Meanwhile, in 2032 water demand will increase, with an average daily demand of 11,697 lt/second, a maximum daily demand of 13,451 lt/second, and a peak hour demand of 20,177 lt/second. Novelty/Originality of this article: This study develops a predictive model integrating demographic, climate, and infrastructure data to project water demand and optimize distribution. This model can be applied across regions to improve water management efficiency.

GDAFormer: Gradient-guided Dual Attention Transformer for Low-Dose CT image denoising

Low-Dose Computed Tomography (LDCT) image denoising is an important problem in the research field of CT, as LDCT images exhibit significant noise and artifacts compared to normal-dose CT images. To address this issue, we propose an LDCT image denoising model named Gradient-guided Dual-Attention TransFormer (GDAFormer), which adopts an encoder–decoder structure. Specifically, in the encoding part, the Gradient Denoising Module (GDM) is utilized to obtain the clean gradient map for effectively extracting edge and structure features, and they are fused with LDCT image features by Cross-Attention Transformer (CAT). The decoding part employs a Dual Self-Attention Transformer (DSAT) to capture inter-channel correlations within the fused feature maps and pixel-level correlations within individual channels, thereby improving denoising performance. Experimental results on the MayoLDCT dataset demonstrate that our proposed GDAFormer achieves competitive performance in terms of quantitative metrics and visual perceptual quality.

Residual Entropy as a Diagnostic and Stopping Metric for CLEAN

We propose the use of entropy, measured from the spatial and flux distribution of pixels in the residual image, as a potential diagnostic and stopping metric for the CLEAN algorithm. Despite its broad success as the standard deconvolution approach in radio interferometry, finding the optimum stopping point for the iterative CLEAN algorithm is still a challenge. We show that the entropy of the residual image, measured during the final stages of CLEAN, can be computed without prior knowledge of the source structure or expected noise levels, and that finding the point of maximum entropy as a measure of randomness in the residual image serves as a robust stopping criterion. We also find that, when compared to the expected thermal noise in the image, the maximum entropy of the residuals is a useful diagnostic that can reveal the presence of data editing, calibration, or deconvolution issues that may limit the fidelity of the final CLEAN map.

A hybrid framework for robust dynamic 3D point clouds removal

When robot creates a map, dynamic objects can change the space and render the map unusable for navigation. Additionally, the vertical resolution of a VLP-16 LiDAR may be insufficient, making dynamic point removal challenging. To address these challenges, we propose a novel method for dynamic point detection and removal consisting of four components. Firstly, we introduce a multi-resolution heightmap to enhance the efficiency and precision of dynamic point recognition by segmenting ground points. Secondly, we address the issue of limited vertical resolution by fusing multiple scans to simulate additional scan lines and leveraging a multi-resolution range image for precise dynamic point elimination. Thirdly, we apply clustering and principal component analysis-based techniques to compute eigenvectors, facilitating the correction of misclassified static points. Lastly, we propose the utilization of a three-dimensional bounding box strategy to reinforce the monitoring of small static clusters with elevated probabilities of misclassification. These four components complement each other and are executed sequentially. We evaluated our method for both dynamic point removal and ground segmentation on the KITTI dataset and real-world environments. The results demonstrate that our method outperforms baseline methods and generates clean maps.

RGB-D SLAM in indoor dynamic environments with two channels based on scenario classification

In visual simultaneous localization and mapping (SLAM) systems, the limitations of the assumption of scene rigidity are usually broken by using learning-based or geometry-based methods. However, learning-based methods usually have a high time cost, and geometry-based methods usually do not result in clean maps which are useful for advanced robotic applications. In this paper, an RGB-D SLAM in indoor dynamic environments with two channels that classifies frames as slightly and highly dynamic scenarios based on matching accuracy is proposed. And a geometric constraint based on Hamming distance is proposed to improve the effectiveness of matching accuracy as a basis for scenario classification. Dynamic features are detected by affine consistency constraint and semantic method. The semantic method is only used for highly dynamic scenarios to reduce the time cost of dynamic feature detection and provide a basis for mapping. Furthermore, an improved adaptive threshold algorithm is proposed to improve the robustness of feature matching. The proposed method is evaluated in the TUM RGB-D dataset and a real scenario. The experimental results demonstrate that the proposed method achieves highly accurate tracking with appreciable time cost in both slightly and highly indoor dynamic environments while obtaining effective maps.

Highly structured turbulence in high-mass star formation: An evolved infrared-dark cloud G35.20-0.74 N

Context. Massive stars are generally believed to form in environments characterized by supersonic turbulence. However, recent observations challenge this traditional view. High-spatial- and spectral-resolution observations of the Orion Molecular Cloud (OMC, the closest massive star formation region) and an infrared-dark cloud (IRDC) G35.39 (a typical distant massive star formation region) show a resolution-dependent turbulence, and that high-mass stars are forming exclusively in subsonic to transonic cores in those clouds. These studies demand a re-evaluation of the role of turbulence in massive star formation. Aims. We aim to study the turbulence in a typical massive-star-forming region G35.20-0.74 N (G35.20 in short) with sufficient spatial resolution to resolve the thermal Jeans length, and sufficient spectral resolution to resolve the thermal line width. Methods. We use the Atacama Large Millimeter/submillimeter Array (ALMA) dust continuum emission to resolve fragmentation, the Karl G. Jansky Very Large Array (JVLA) 1.2 cm continuum to trace ionized gas, and JVLA NH3 (1,1) to (7,7) inversion transition lines to trace line width, temperature, and dynamics. We fit those lines and remove line broadening due to channel width, thermal pressure, and velocity gradient to obtain a clean map of intrinsic turbulence. Results. We find that (1) the turbulence in G35.20 is overall supersonic, with mean and median Mach numbers 3.7 and 2.8, respectively. (2) Mach number decreases from 6–7 at a 0.1 pc scale to less than 3 toward the central cores at a 0.01 pc scale. (3) The central ALMA cores appear to be decoupled from the host filament, which is made evident by an opposite velocity gradient and significantly reduced turbulence. Because of intense star-formation activity in G35.20 (as compared to the relatively young and quiescent IRDC G35.39), the supersonic turbulence is likely replenished by protostellar outflows. G35.20 is therefore representative of an evolved form of IRDC G35.39. More observations of a sample of IRDCs are highly demanded in order to further investigate the role of turbulence in the initial conditions required for massive-star formation.

Clean Water Mapping as a Transdisciplinary Disaster Mitigation Effort on the Mekong Riverbank: A descriptive study

On the Mekong Riverbank, clean water mapping is a commonly utilized tool for disaster risk reduction and public health promotion initiatives. The purpose of this research is to investigate the possibility of clean water mapping as a transnational catastrophe mitigation initiative. A comprehensive literature analysis will be conducted to identify existing research and data on clean water maps, disaster risk reduction, and transnational water governance on the Mekong riverbank. The semi-structured interviews reveal current challenges and opportunities associated with cleaning water mapping, disaster risk prevention, and water governance, such as technical and institutional barriers, the need for collaborative and transdisciplinary approaches, and the complexity of transboundary water governance. The qualitative data demonstrate the potential benefits of cleaning water for disaster mitigation efforts, such as identifying high-risk areas and developing early warning systems for water-related disasters, as well as having broader public health benefits beyond the context of disasters. With the proper plans and tactics in place, clean water can make a big contribution to the region's long-term growth and people's well-being.

A foreground model-independent Bayesian CMB temperature and polarization signal reconstruction and cosmological parameter estimation over large angular scales

ABSTRACT Recent CMB observations have resulted in very precise observational data. A robust and reliable CMB reconstruction technique can lead to efficient estimation of the cosmological parameters. We demonstrate the performance of our methodology using simulated temperature and polarization observations using cosmic variance-limited future-generation PRISM satellite mission. We generate samples from the joint distribution by implementing the CMB inverse covariance weighted internal-linear-combination (ILC) with the Gibbs sampling technique. We use the Python Sky Model (PySM), d4f1s1 to generate the realistic foreground templates. The synchrotron emission is parametrized by a spatially varying spectral index, whereas the thermal dust emission is described as a two-component dust model. We estimate the marginalized densities of CMB signal and theoretical angular power spectrum utilizing the samples from the entire posterior distribution. The best-fitting cleaned CMB map and the corresponding angular power spectrum are consistent with the CMB realization and the sky angular power spectrum, implying an efficient foreground-minimized reconstruction. The likelihood function estimated by making use of the Blackwell–Rao estimator is used for the estimation of cosmological parameters. Our methodology can estimate the tensor-to-scalar ratio r ≥ 0.0075 for the chosen foreground models and the instrumental noise levels. Our current work demonstrates an analysis pipeline starting from the reliable estimation of CMB signal and its angular power spectrum to the case of cosmological parameter estimation using the foreground model-independent Gibbs–ILC method.

Level correlations of CMB temperature angular power spectrum

If the principle of statistical isotropy is valid, then the angular power spectrum (APS) of cosmic microwave background (CMB) radiation is uncorrelated between different multipoles. We propose a novel technique to analyse any possible correlations of the foreground-cleaned CMB temperature APS measures (\(C_{\ell }\) and \({\mathcal {D}}_\ell =({\ell (\ell +1)})/({2\pi })C_\ell \)). This is motivated by the behaviour of level spacings between random matrix eigenvalues. This method helps to distinguish uncorrelated statistically isotropic CMB APS from correlated APS, where the latter can arise due to breakdown of isotropy or presence of some residual systematics in the foreground cleaned CMB maps. Spacings of statistically isotropic CMB \(C_\ell \)’s and \({\mathcal {D}}_\ell \)’s are seen to closely obey Poisson statistics and introduction of correlations, changes the distribution to appropriate Wigner–Dyson statistics. For foreground-cleaned CMB, we employ the average spacing of consecutive multipole APS for multipoles \(\in [2,31]\). This estimator is sensitive to departures from the null hypothesis of zero correlations between the APS measures of statistically isotropic CMB. We study full sky WMAP 9-year ILC and 2018 Planck foreground-cleaned maps (Commander, NILC and SMICA). Sans parity distinctions, average spacings are in good agreement with theoretical expectation. With parity distinctions, even multipoles indicate unusually low average spacings for both \(C_\ell \)’s (at \(\ge \)98.86% C.L.) and \({\mathcal {D}}_\ell \)’s (at \({\ge }\)95.07% C.L.). We use an inpainting method based on constrained Gaussian realisations and show that for the Planck U73 and WMAP KQ75 masks, all the foreground-cleaned inpainted CMB maps robustly confirm the existence of such unusually low average spacings of even multipole APS. In addition, this signal is independent of the non-Gaussian cold spot.

Static map generation from 3D LiDAR point clouds exploiting ground segmentation

A clean and reliable map of the environment is key for a variety of robotic tasks including localization, path planning, and navigation. Dynamic objects are an inherent part of our world, but their presence often deteriorates the performance of various mapping algorithms. This not only makes it important but necessary to remove these dynamic points from the map before they can be used for other tasks such as path planning. In this paper, we address the problem of building maps of the static aspects of the world by detecting and removing dynamic points from the source point clouds. We target a map cleaning approach that removes the dynamic points and maintains a high quality map of the static part of the world. To this end, we propose a novel offline ground segmentation method and integrate it into the OctoMap to better distinguish between the moving objects and static road backgrounds. We evaluate our approach using SemanticKITTI for both, dynamic object removal and ground segmentation algorithms as well as on the Apollo dataset. The evaluation results show that our method outperforms the baseline methods in both tasks and achieves good performance in generating clean maps over different datasets without any change in the parameters.

A Partial-sky Gibbs ILC Approach for the Estimation of CMB Posterior over Large Angular Scales of the Sky

In this article we present a formalism for incorporating the partial-sky maps into the Gibbs ILC algorithm to estimate the joint posterior density of the cosmic microwave background (CMB) signal and the theoretical CMB angular power spectrum given the observed CMB maps. In order to generate the partial-sky maps, we mask all the observed CMB maps provided by the WMAP and Planck satellite full-sky mission using a mask that removes 22% of the entire sky. The mask we use is based on the strength of the thermal dust emissions in the Planck 353 GHz map. While implementing the Gibbs ILC method on the partial-sky maps, we convert the partial-sky-cleaned angular power spectrum into the full-sky angular power spectrum using the mode-mode coupling matrix estimated from the smoothed mask. The main products of our analysis are a partial-sky cleaned best-fit CMB map and an estimate of the underlying full-sky theoretical CMB angular power spectrum in the multipole range 2 ≤ ℓ ≤ 32 along with their error estimates. We validate the method by performing detailed Monte Carlo simulations after using realistic models of foregrounds and detector noise consistent with the WMAP and Planck frequency channels used in our analysis. We can estimate the posterior density and full-sky theoretical CMB angular power spectrum without any need to explicitly model the foreground components from partial-sky maps using our method. Another important feature of this method is that the power spectrum results along with the error estimates can be directly used for cosmological parameter estimations.

Real-time infrared small target detection network and accelerator design

Infrared small target detection (ISTD) plays an important role in military and civil fields. However, various complex backgrounds in real scenarios significantly degrade the ISTD performance. This study proposes an elaborate convolutional neural network (CNN) for ISTD, called small target (ST)-Net, by modeling the task as an image-to-image translation problem. The input image is translated into a clean output map, where only the target remains. ST-Net is a lightweight network with 13 convolutional layers. It uses residual connections to smoothen the forward and backward propagation. The proposed detection method is validated on two different types of widely used infrared small target datasets. Extensive experiments demonstrate that ST-Net could effectively boost the number of correct detections and reduce the number of missed and false detections. Furthermore, a quantized model training method is presented to find as many circuit design benefits as possible while ensuring that the performance degradation of the model is within an acceptable range. Then a high-throughput and energy-efficient accelerator for ST-Net is designed based on the binarized model. Simulation results under CMOS 28 nm technology show that the proposed accelerator can achieve 56 FPS, which meets the real-time requirement, while the power consumption is as low as 48.7 mW. In addition, the accelerator is implemented on FPGA, which can be deployed into an embedded platform.

Isotropy statistics of CMB hot and cold spots

Statistical Isotropy of the Cosmic Microwave Background (CMB) radiation has been studied and debated extensively in recent years. Under this assumption, the hot spots and cold spots of the CMB are expected to be uniformly distributed over a 2-sphere. We use the orientation matrix, first proposed by Watson (1965) and Scheidegger (1965) and associated shape and strength parameters (Woodcock, 1977) to analyse whether the hot and cold spots of the observed CMB temperature anisotropy field are uniformly placed. We demonstrate the usefulness of our estimators by using simulated toy models containing non-uniform data. We apply our method on several foreground minimized CMB maps observed by WMAP and Planck over large angular scales. The shape and strength parameters constrain geometric features of possible deviations from uniformity (isotropy) and the power of the anomalous signal. We find that distributions of hot or cold spots in cleaned maps show no unusual signature of clustering or girdling. Instead, we notice a strikingly uniform distribution of hot spots over the full sky. The signal remains robust with respect to the four cleaned maps used and presence or absence of the non-Gaussian cold spot (NGCS). On the partial sky with WMAP KQ75 and Planck U73 masks we find anomalously low strength of non-uniformity for cold spots which is found to be robust with respect to various cleaning methods, masks applied, instruments, frequencies, and the presence or absence of the NGCS. Interestingly we find that the signal of anomalously weak non-uniformity could be due to contributions from the quadrupole and octupole and may be related with the low CMB temperature variance anomaly.

A foreground model independent estimation of joint posterior of CMB E-mode polarization over large angular scales

The advent of precision cosmology has led to a very accurate observational data. In order to extract cosmological information, we therefore need robust and accurate Cosmic Microwave Background (CMB) reconstruction techniques with reliable statistical error estimates associated with them. We show the performance of our algorithm using simulations of the future Probe of Inflation and Cosmic Origins (PICO) missions. To generate samples from the joint distribution, we employ the internal linear combination (ILC) technique with prior information of CMB E-mode covariance matrix augmented by a Gibbs sampling technique of Sudevan and Saha 2020. We estimate the marginalized densities of [Formula: see text] and [Formula: see text] using the samples from full-posterior. The best-fit cleaned E-mode map and the corresponding angular power spectrum are consistent with the input E-mode map and the sky power spectrum implying accurate reconstruction. Using the samples [Formula: see text] of all Gibbs chains, we estimate the likelihood function [Formula: see text]) of any arbitrary [Formula: see text] given simulated observed maps (data, D) of PICO mission using a Blackwell–Rao estimator. The likelihood function can be seamlessly integrated to the cosmological parameter estimation method. Apart from producing an accurate estimate of E-mode signal over large angular scales, our method also builds a connection between the component reconstruction and reliable cosmological parameter estimation using CMB E-mode observations over large angular scales. The entire method does not assume any explicit models for E-mode foreground components in order to remove them, which is an attractive property since foreground modeling uncertainty does not pose as a challenge in this case.

Anharmonic Motion in the Crystal Structure of 2-Mercaptopyridone

With the present study, we revise earlier results about the title compound 2-mercaptopyridone in which the deformation density was determined from an X–N study in 1982 (X-ray, neutron diffraction). In the current X–X study (X-ray, X-ray), anharmonic motion parameters for the atoms were included and it resulted in a very clean deformation density map without the unexplainable features of the literature results. The presence of anharmonic effects is supported by a study of the thermal expansion in the temperature range 100–260 K. A topological study of the electron density confirms a bond length alternation in the six-membered ring. The calculation of the electrostatic potential indicates that the N–H⋯S hydrogen bond is an electrostatic interaction, and that other attractive intermolecular contacts are probably dispersive.

In-flight polarization angle calibration for LiteBIRD: blind challenge and cosmological implications

We present a demonstration of the in-flight polarization angle calibration for the JAXA/ISAS second strategic large class mission, LiteBIRD, and estimate its impact on the measurement of the tensor-to-scalar ratio parameter, r, using simulated data.We generate a set of simulated sky maps with CMB and polarized foreground emission, and inject instrumental noise and polarization angle offsets to the 22 (partially overlapping) LiteBIRD frequency channels. Our in-flight angle calibration relies on nulling the EB cross correlation of the polarized signal in each channel. This calibration step has been carried out by two independent groups with a blind analysis, allowing an accuracy of the order of a few arc-minutes to be reached on the estimate of the angle offsets. Both the corrected and uncorrected multi-frequency maps are propagated through the foreground cleaning step, with the goal of computing clean CMB maps. We employ two component separation algorithms, the Bayesian-Separation of Components and Residuals Estimate Tool (B-SeCRET), and the Needlet Internal Linear Combination (NILC). We find that the recovered CMB maps obtained with algorithms that do not make any assumptions about the foreground properties, such as NILC, are only mildly affected by the angle miscalibration. However, polarization angle offsets strongly bias results obtained with the parametric fitting method. Once the miscalibration angles are corrected by EB nulling prior to the component separation, both component separation algorithms result in an unbiased estimation of the r parameter. While this work is motivated by the conceptual design study for LiteBIRD, its framework can be broadly applied to any CMB polarization experiment. In particular, the combination of simulation plus blind analysis provides a robust forecast by taking into account not only detector sensitivity but also systematic effects.

Application of genetic algorithm to estimate the large angular scale features of cosmic microwave background

ABSTRACT Genetic Algorithm (GA) – motivated by natural evolution – is a robust method to estimate the global optimal solutions of problems involving multiple objective functions. In this article, for the first time, we apply GA to reconstruct the CMB temperature anisotropy map over large angular scales of the sky using (internal) linear combination (ILC) of the final-year WMAP and Planck satellite observations. To avoid getting trapped into a local minimum, we implement the GA with generous diversity in the populations by selecting pairs with diverse fitness coefficients and by introducing a small but significant amount of mutation of genes. We find that the new GA-ILC method produces a clean map which agrees very well with that obtained using the exact analytical expression of weights in ILC. By performing extensive Monte Carlo simulations of the CMB reconstruction using the GA-ILC algorithm, we find that residual foregrounds in the cleaned map are minimal and tend to occupy localized regions along the galactic plane. The CMB angular power spectrum shows no indication of any bias in the entire multipole range 2 ≤ ℓ ≤ 32 studied in this work. The error in the CMB angular power spectrum is also minimal and given entirely by the cosmic-variance-induced error. Our results agree well with those obtained by various other reconstruction methods by different research groups. This problem-independent robust GA-ILC method provides a flexible way towards the complex and challenging task of CMB component reconstruction in cosmology.

Blitz-SLAM: A semantic SLAM in dynamic environments

Static environment is a prerequisite for most of visual simultaneous localization and mapping systems. Such a strong assumption limits the practical application of most existing SLAM systems. When moving objects enter the camera’s view field, dynamic matching points will directly interrupt the camera localization, and the noise blocks formed by moving objects will contaminate the constructed map. In this paper, a semantic SLAM system working in indoor dynamic environments named Blitz-SLAM is proposed. The noise blocks in the local point cloud are removed by combining the advantages of semantic and geometric information of mask, RGB and depth images. The global point cloud map can be obtained by merging the local point clouds. We evaluate Blitz-SLAM on the TUM RGB-D dataset and in the real-world environment. The experimental results demonstrate that Blitz-SLAM can work robustly in dynamic environments and generate a clean and accurate global point cloud map simultaneously.

Mitigating contamination in LSS surveys: a comparison of methods

ABSTRACT Future large-scale structure surveys will measure the locations and shapes of billions of galaxies. The precision of such catalogues will require meticulous treatment of systematic contamination of the observed fields. We compare several existing methods for removing such systematics from galaxy clustering measurements. We show how all the methods, including the popular pseudo-Cℓ Mode Projection and Template Subtraction methods, can be interpreted under a common regression framework and use this to suggest improved estimators. We show how methods designed to mitigate systematics in the power spectrum can be used to produce clean maps, which are necessary for cosmological analyses beyond the power spectrum, and we extend current methods to treat the next-order multiplicative contamination in observed maps and power spectra, which reduced power spectrum errors from $\Delta \chi ^2_{\rm C_\ell }\simeq 10$ to ≃ 1 in simulated analyses. Two new mitigation methods are proposed, which incorporate desirable features of current state-of-the-art methods while being simpler to implement. Investigating the performance of all the methods on a common set of simulated measurements from Year 5 of the Dark Energy Survey, we test their robustness to various analysis cases. Our proposed methods produce improved maps and power spectra when compared to current methods, while requiring almost no user tuning. We end with recommendations for systematics mitigation in future surveys, and note that the methods presented are generally applicable beyond the galaxy distribution to any field with spatial systematics.